Tools implemented in bob.bio.face¶

Summary¶

Databases¶

`bob.bio.face.database.ARFaceBioDatabase`([…])	ARFace database implementation of `bob.bio.base.database.BioDatabase` interface.
`bob.bio.face.database.AtntBioDatabase`([…])	ATNT database implementation of `bob.bio.base.database.BioDatabase` interface.
`bob.bio.face.database.BancaBioDatabase`([…])	BANCA database implementation of `bob.bio.base.database.ZTBioDatabase` interface.
`bob.bio.face.database.MobioBioDatabase`([…])	MOBIO database implementation of bob.bio.base.database.ZTBioDatabase interface.
`bob.bio.face.database.CaspealBioDatabase`([…])	Caspeal database implementation of `bob.bio.base.database.BioDatabase` interface.
`bob.bio.face.database.ReplayBioDatabase`(**kwargs)	Replay attack database implementation of bob.bio.base.database.BioDatabase interface.
`bob.bio.face.database.ReplayMobileBioDatabase`([…])	ReplayMobile database implementation of `bob.bio.base.database.BioDatabase` interface.
`bob.bio.face.database.MsuMfsdModBioDatabase`([…])	MsuMfsdMod database implementation of `bob.bio.base.database.BioDatabase` interface.
`bob.bio.face.database.GBUBioDatabase`([…])	GBU database implementation of `bob.bio.base.database.BioDatabase` interface.
`bob.bio.face.database.LFWBioDatabase`([…])	LFW database implementation of bob.bio.base.database.Database interface.
`bob.bio.face.database.MultipieBioDatabase`([…])	Multipie database implementation of bob.bio.base.database.Database interface.
`bob.bio.face.database.IJBABioDatabase`([…])	IJBA database implementation of `bob.bio.base.database.BioDatabase` interface.
`bob.bio.face.database.XM2VTSBioDatabase`([…])	XM2VTS database implementation of bob.bio.base.database.Database interface.
`bob.bio.face.database.FRGCBioDatabase`([…])	FRGC database implementation of `bob.bio.base.database.BioDatabase` interface.
`bob.bio.face.database.SCFaceBioDatabase`([…])	SCFace database implementation of bob.bio.base.database.ZTDatabase interface.
`bob.bio.face.database.FargoBioDatabase`([…])	FARGO database implementation of `bob.bio.base.database.BioDatabase` interface.

Face Image Annotators¶

`bob.bio.face.annotator.Base`(**kwargs)	Base class for all face annotators
`bob.bio.face.annotator.BobIpFacedetect`([…])	Annotator using bob.ip.facedetect Provides topleft and bottomright annoations.
`bob.bio.face.annotator.BobIpFlandmark`(**kwargs)	Annotator using bob.ip.flandmark.
`bob.bio.face.annotator.BobIpMTCNN`(**kwargs)	Annotator using bob.ip.mtcnn

Image Preprocessors¶

`bob.bio.face.preprocessor.Base`([dtype, …])	Performs color space adaptations and data type corrections for the given image.
`bob.bio.face.preprocessor.FaceCrop`(…[, …])	Crops the face according to the given annotations.
`bob.bio.face.preprocessor.FaceDetect`(…[, …])	Performs a face detection (and facial landmark localization) in the given image and crops the face.
`bob.bio.face.preprocessor.TanTriggs`(face_cropper)	Crops the face (if desired) and applies Tan&Triggs algorithm [TT10] to photometrically enhance the image.
`bob.bio.face.preprocessor.HistogramEqualization`(…)	Crops the face (if desired) and performs histogram equalization to photometrically enhance the image.
`bob.bio.face.preprocessor.SelfQuotientImage`(…)	Crops the face (if desired) and applies self quotient image algorithm [WLW04] to photometrically enhance the image.
`bob.bio.face.preprocessor.INormLBP`(face_cropper)	Performs I-Norm LBP on the given image

Image Feature Extractors¶

`bob.bio.face.extractor.Eigenface`(…)	Performs a principal component analysis (PCA) on the given data.
`bob.bio.face.extractor.DCTBlocks`([…])	Extracts Discrete Cosine Transform (DCT) features from (overlapping) image blocks.
`bob.bio.face.extractor.GridGraph`([…])	Extracts Gabor jets in a grid structure [GHW12] using functionalities from bob.ip.gabor.
`bob.bio.face.extractor.LGBPHS`(block_size[, …])	Extracts Local Gabor Binary Pattern Histogram Sequences (LGBPHS) [ZSG05] from the images, using functionality from bob.ip.base and bob.ip.gabor.

Face Recognition Algorithms¶

`bob.bio.face.algorithm.GaborJet`(…[, …])	Computes a comparison of lists of Gabor jets using a similarity function of `bob.ip.gabor.Similarity`.
`bob.bio.face.algorithm.Histogram`([…])	Computes the distance between histogram sequences.

Databases¶

class bob.bio.face.database.ARFaceBioDatabase(original_directory=None, original_extension='.ppm', **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

ARFace database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to ARFACE database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

class bob.bio.face.database.AtntBioDatabase(original_directory=None, original_extension='.pgm', **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

ATNT database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of the database interface, which directly talks to ATNT database, for verification experiments (good to use in bob.bio.base framework).

annotations(file)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

class bob.bio.face.database.BancaBioDatabase(original_directory=None, original_extension=None, **kwargs)¶

Bases: bob.bio.base.database.ZTBioDatabase

BANCA database implementation of bob.bio.base.database.ZTBioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to Banca database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

tmodel_ids_with_protocol(protocol=None, groups=None, **kwargs)[source]¶

This function returns the ids of the T-Norm models of the given groups for the given protocol.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.

tobjects(groups=None, protocol=None, model_ids=None, **kwargs)[source]¶

This function returns the File objects of the T-Norm models of the given groups for the given protocol and the given model ids.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

zobjects(groups=None, protocol=None, **kwargs)[source]¶

This function returns the File objects of the Z-Norm impostor files of the given groups for the given protocol.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.

class bob.bio.face.database.CaspealBioDatabase(original_directory=None, original_extension='.tif', **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

Caspeal database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to Caspeal database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

class bob.bio.face.database.FRGCBioDatabase(original_directory=None, original_extension='.jpg', **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

FRGC database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of the low-level database interface, which directly talks to FRGC database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

class bob.bio.face.database.FaceBioFile(client_id, path, file_id, **kwargs)¶: Bases: bob.bio.base.database.BioFile

class bob.bio.face.database.FargoBioDatabase(original_directory=None, original_extension='.png', protocol='mc-rgb', **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

FARGO database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of the database interface, which directly talks to ATNT database, for verification experiments (good to use in bob.bio.base framework).

annotations(file)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, purposes=None, protocol=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

class bob.bio.face.database.GBUBioDatabase(original_directory=None, original_extension='.jpg', **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

GBU database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to GBU database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

class bob.bio.face.database.IJBABioDatabase(original_directory=None, annotation_directory=None, original_extension=None, **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

IJBA database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to IJBA database, for verification experiments (good to use in bob.bio.base framework).

annotation_directory¶

annotations(biofile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

client_id_from_model_id(model_id, group='dev')[source]¶: Return the client id associated with the given model id. In this base class implementation, it is assumed that only one model is enrolled for each client and, thus, client id and model id are identical. All key word arguments are ignored. Please override this function in derived class implementations to change this behavior.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

object_sets(groups=None, protocol='search_split1', purposes=None, model_ids=None)[source]¶

This function returns lists of FileSet objects, which fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

objects(groups=None, protocol='search_split1', purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

original_file_names(files)[source]¶

Returns the full path of the original data of the given File objects.

Parameters:	files (list of `bob.db.base.File`) – The list of file object to retrieve the original data file names for.
Returns:	The paths extracted for the files, in the same order.
Return type:	list of `str`

uses_probe_file_sets()[source]¶: Defines if, for the current protocol, the database uses several probe files to generate a score. Returns True if the given protocol specifies file sets for probes, instead of a single probe file. In this default implementation, False is returned, throughout. If you need different behavior, please overload this function in your derived class.

class bob.bio.face.database.IJBBBioDatabase(original_directory=None, annotation_directory=None, original_extension=None, **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

IJBB database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to IJBB database, for verification experiments (good to use in bob.bio.base framework).

annotation_directory¶

annotations(biofile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

client_id_from_model_id(model_id, group='dev')[source]¶: Return the client id associated with the given model id. In this base class implementation, it is assumed that only one model is enrolled for each client and, thus, client id and model id are identical. All key word arguments are ignored. Please override this function in derived class implementations to change this behavior.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

object_sets(groups=None, protocol='1:1', purposes=None, model_ids=None)[source]¶

This function returns lists of FileSet objects, which fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

objects(groups=None, protocol='1:1', purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

original_file_names(files)[source]¶

Returns the full path of the original data of the given File objects.

Parameters:	files (list of `bob.db.base.File`) – The list of file object to retrieve the original data file names for.
Returns:	The paths extracted for the files, in the same order.
Return type:	list of `str`

uses_probe_file_sets()[source]¶: Defines if, for the current protocol, the database uses several probe files to generate a score. Returns True if the given protocol specifies file sets for probes, instead of a single probe file. In this default implementation, False is returned, throughout. If you need different behavior, please overload this function in your derived class.

class bob.bio.face.database.IJBCBioDatabase(original_directory=None, annotation_directory=None, original_extension=None, **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

IJBC database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to IJBC database, for verification experiments (good to use in bob.bio.base framework).

annotation_directory¶

annotations(biofile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

client_id_from_model_id(model_id, group='dev')[source]¶: Return the client id associated with the given model id. In this base class implementation, it is assumed that only one model is enrolled for each client and, thus, client id and model id are identical. All key word arguments are ignored. Please override this function in derived class implementations to change this behavior.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

object_sets(groups=None, protocol='1:1', purposes=None, model_ids=None)[source]¶

This function returns lists of FileSet objects, which fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

objects(groups=None, protocol='1:1', purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

original_file_names(files)[source]¶

Returns the full path of the original data of the given File objects.

Parameters:	files (list of `bob.db.base.File`) – The list of file object to retrieve the original data file names for.
Returns:	The paths extracted for the files, in the same order.
Return type:	list of `str`

uses_probe_file_sets()[source]¶: Defines if, for the current protocol, the database uses several probe files to generate a score. Returns True if the given protocol specifies file sets for probes, instead of a single probe file. In this default implementation, False is returned, throughout. If you need different behavior, please overload this function in your derived class.

class bob.bio.face.database.LFWBioDatabase(original_directory=None, original_extension='.jpg', annotation_type=None, **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

LFW database implementation of bob.bio.base.database.Database interface. It is an extension of an SQL-based database interface, which directly talks to LFW database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

client_id_from_model_id(model_id, group='dev')[source]¶: Return the client id associated with the given model id. In this base class implementation, it is assumed that only one model is enrolled for each client and, thus, client id and model id are identical. All key word arguments are ignored. Please override this function in derived class implementations to change this behavior.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

class bob.bio.face.database.MobioBioDatabase(original_directory=None, original_extension=None, annotation_directory=None, annotation_extension='.pos', **kwargs)¶

Bases: bob.bio.base.database.ZTBioDatabase

MOBIO database implementation of bob.bio.base.database.ZTBioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to Mobio database, for verification experiments (good to use in bob.bio.base framework).

annotation_directory¶

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

groups(protocol=None, **kwargs)[source]¶

Returns the names of all registered groups in the database

Keyword parameters:

protocol: str: The protocol for which the groups should be retrieved. If you do not have protocols defined, just ignore this field.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

tmodel_ids_with_protocol(protocol=None, groups=None, **kwargs)[source]¶

This function returns the ids of the T-Norm models of the given groups for the given protocol.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.

tobjects(groups=None, protocol=None, model_ids=None, **kwargs)[source]¶

This function returns the File objects of the T-Norm models of the given groups for the given protocol and the given model ids.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

zobjects(groups=None, protocol=None, **kwargs)[source]¶

This function returns the File objects of the Z-Norm impostor files of the given groups for the given protocol.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.

class bob.bio.face.database.MsuMfsdModBioDatabase(max_number_of_frames=None, **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

MsuMfsdMod database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to MsuMfsdMod database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶: Will return the bounding box annotation of nth frame of the video.

arrange_by_client(files) → files_by_client[source]¶

Arranges the given list of files by client id. This function returns a list of lists of File’s.

Parameters:

files : bob.bio.base.database.BioFile: A list of files that should be split up by BioFile.client_id.

Returns:

files_by_client : [[bob.bio.base.database.BioFile]]: The list of lists of files, where each sub-list groups the files with the same BioFile.client_id

groups()[source]¶

Returns the names of all registered groups in the database

Keyword parameters:

protocol: str: The protocol for which the groups should be retrieved. If you do not have protocols defined, just ignore this field.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

protocol_names()[source]¶

class bob.bio.face.database.MultipieBioDatabase(original_directory=None, original_extension='.png', annotation_directory=None, annotation_extension='.pos', **kwargs)¶

Bases: bob.bio.base.database.ZTBioDatabase

Multipie database implementation of bob.bio.base.database.Database interface. It is an extension of an SQL-based database interface, which directly talks to Multipie database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

tmodel_ids_with_protocol(protocol=None, groups=None, **kwargs)[source]¶

This function returns the ids of the T-Norm models of the given groups for the given protocol.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.

tobjects(groups=None, protocol=None, model_ids=None, **kwargs)[source]¶

This function returns the File objects of the T-Norm models of the given groups for the given protocol and the given model ids.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

zobjects(groups=None, protocol=None, **kwargs)[source]¶

This function returns the File objects of the Z-Norm impostor files of the given groups for the given protocol.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.

class bob.bio.face.database.ReplayBioDatabase(**kwargs)¶

Bases: bob.bio.base.database.BioDatabase

Replay attack database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to Replay database, for verification experiments (good to use in bob.bio.base framework). It also implements a kind of hack so that you can run vulnerability analysis with it.

annotations(file)[source]¶: Will return the bounding box annotation of 10th frame of the video.

arrange_by_client(files) → files_by_client[source]¶

Arranges the given list of files by client id. This function returns a list of lists of File’s.

Parameters:

files : bob.bio.base.database.BioFile: A list of files that should be split up by BioFile.client_id.

Returns:

files_by_client : [[bob.bio.base.database.BioFile]]: The list of lists of files, where each sub-list groups the files with the same BioFile.client_id

groups()[source]¶

Returns the names of all registered groups in the database

Keyword parameters:

protocol: str: The protocol for which the groups should be retrieved. If you do not have protocols defined, just ignore this field.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

protocol_names()[source]¶: Returns all registered protocol names Here I am going to hack and double the number of protocols with -licit and -spoof. This is done for running vulnerability analysis

class bob.bio.face.database.ReplayMobileBioDatabase(max_number_of_frames=None, annotation_directory=None, annotation_extension='.json', annotation_type='json', original_directory=None, original_extension='.mov', name='replay-mobile', **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

ReplayMobile database implementation of bob.bio.base.database.BioDatabase interface. It is an extension of an SQL-based database interface, which directly talks to ReplayMobile database, for verification experiments (good to use in bob.bio.base framework).

annotation_directory¶

annotation_extension¶

annotation_type¶

annotations(myfile)[source]¶: Will return the bounding box annotation of nth frame of the video.

arrange_by_client(files) → files_by_client[source]¶

Arranges the given list of files by client id. This function returns a list of lists of File’s.

Parameters:

files : bob.bio.base.database.BioFile: A list of files that should be split up by BioFile.client_id.

Returns:

files_by_client : [[bob.bio.base.database.BioFile]]: The list of lists of files, where each sub-list groups the files with the same BioFile.client_id

groups()[source]¶

Returns the names of all registered groups in the database

Keyword parameters:

protocol: str: The protocol for which the groups should be retrieved. If you do not have protocols defined, just ignore this field.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

original_extension¶

protocol_names()[source]¶

class bob.bio.face.database.SCFaceBioDatabase(original_directory=None, original_extension='.jpg', **kwargs)¶

Bases: bob.bio.base.database.ZTBioDatabase

SCFace database implementation of bob.bio.base.database.ZTDatabase interface. It is an extension of an SQL-based database interface, which directly talks to SCFace database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

tmodel_ids_with_protocol(protocol=None, groups=None, **kwargs)[source]¶

This function returns the ids of the T-Norm models of the given groups for the given protocol.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.

tobjects(groups=None, protocol=None, model_ids=None, **kwargs)[source]¶

This function returns the File objects of the T-Norm models of the given groups for the given protocol and the given model ids.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

zobjects(groups=None, protocol=None, **kwargs)[source]¶

This function returns the File objects of the Z-Norm impostor files of the given groups for the given protocol.

Keyword parameters:

groups : str or [str]: The groups of which the model ids should be returned. Usually, groups are one or more elements of (‘dev’, ‘eval’)
protocol : str: The protocol for which the model ids should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.

class bob.bio.face.database.XM2VTSBioDatabase(original_directory=None, original_extension='.ppm', **kwargs)¶

Bases: bob.bio.base.database.BioDatabase

XM2VTS database implementation of bob.bio.base.database.Database interface. It is an extension of an SQL-based database interface, which directly talks to XM2VTS database, for verification experiments (good to use in bob.bio.base framework).

annotations(myfile)[source]¶

Returns the annotations for the given File object, if available. You need to override this method in your high-level implementation. If your database does not have annotations, it should return None.

Parameters:

file : bob.bio.base.database.BioFile: The file for which annotations should be returned.

Returns:

annots : dict or None: The annotations for the file, if available.

model_ids_with_protocol(groups = None, protocol = None, **kwargs) → ids[source]¶

Returns a list of model ids for the given groups and given protocol.

Parameters:

groups : one or more of ('world', 'dev', 'eval'): The groups to get the model ids for.

protocol: a protocol name

Returns:

ids : [int] or [str]: The list of (unique) model ids for the given groups.

objects(groups=None, protocol=None, purposes=None, model_ids=None, **kwargs)[source]¶

This function returns a list of bob.bio.base.database.BioFile objects or the list of objects which inherit from this class. Returned files fulfill the given restrictions.

Keyword parameters:

groups : str or [str]: The groups of which the clients should be returned. Usually, groups are one or more elements of (‘world’, ‘dev’, ‘eval’)
protocol: The protocol for which the clients should be retrieved. The protocol is dependent on your database. If you do not have protocols defined, just ignore this field.
purposes : str or [str]: The purposes for which File objects should be retrieved. Usually, purposes are one of (‘enroll’, ‘probe’).
model_ids : [various type]: The model ids for which the File objects should be retrieved. What defines a ‘model id’ is dependent on the database. In cases, where there is only one model per client, model ids and client ids are identical. In cases, where there is one model per file, model ids and file ids are identical. But, there might also be other cases.

original_directory¶

Annotators¶

bob.bio.face.annotator.bounding_box_to_annotations(bbx)[source]¶

Converts bob.ip.facedetect.BoundingBox to dictionary annotations.

Parameters:	bbx (`bob.ip.facedetect.BoundingBox`) – The given bounding box.
Returns:	A dictionary with topleft and bottomright keys.
Return type:	dict

bob.bio.face.annotator.min_face_size_validator(annotations, min_face_size=(32, 32))[source]¶

Validates annotations based on face’s minimal size.

Parameters:	annotations (dict) – The annotations in dictionary format. min_face_size ((`int`, `int`), optional) – The minimal size of a face.
Returns:	True, if the face is large enough.
Return type:	bool

class bob.bio.face.annotator.Base(**kwargs)¶

Bases: bob.bio.base.annotator.Annotator

Base class for all face annotators

annotate(sample, **kwargs)[source]¶

Annotates an image and returns annotations in a dictionary. All annotator should return at least the topleft and bottomright coordinates. Some currently known annotation points such as reye and leye are formalized in bob.bio.face.preprocessor.FaceCrop.

Parameters:	sample (numpy.ndarray) – The image should be a Bob format (#Channels, Height, Width) RGB image. **kwargs – The extra arguments that may be passed.

class bob.bio.face.annotator.BobIpFacedetect(cascade=None, detection_overlap=0.2, distance=2, scale_base=0.9576032806985737, lowest_scale=0.125, eye_estimate=False, **kwargs)¶

Bases: bob.bio.face.annotator.Base

Annotator using bob.ip.facedetect Provides topleft and bottomright annoations.

Parameters:

cascade (bob.ip.facedetect.Cascade) – The file name, where a face detector cascade can be found. If None, the default cascade for frontal faces bob.ip.facedetect.default_cascade is used.
detection_overlap (float) – See bob.ip.facedetect.detect_single_face.
distance (int) – See the Sampling section in the Users Guide of bob.ip.facedetect.
scale_base (float) – See the Sampling section in the Users Guide of bob.ip.facedetect.
lowest_scale (float) – See the Sampling section in the Users Guide of bob.ip.facedetect.
eye_estimate (bool) – If True, expected eye locations are added to the annotations.

annotate(image, **kwargs)[source]¶

Return topleft and bottomright and expected eye positions

Parameters:	image (array) – Image in Bob format RGB image. **kwargs – Ignored.
Returns:	The annotations in a dictionary. The keys are topleft, bottomright, quality, leye, reye.
Return type:	dict

class bob.bio.face.annotator.BobIpFlandmark(**kwargs)¶

Bases: bob.bio.face.annotator.Base

Annotator using bob.ip.flandmark. This annotator needs the topleft and bottomright annotations provided.

Example usage:

>>> from bob.bio.base.annotator import FailSafe
>>> from bob.bio.face.annotator import (
...     BobIpFacedetect, BobIpFlandmark)
>>> annotator = FailSafe(
...     [BobIpFacedetect(), BobIpFlandmark()],
...     required_keys=('reye', 'leye'))

annotate(image, annotations, **kwargs)[source]¶

Annotates an image.

Parameters:	image (array) – Image in Bob format RGB. annotations (dict) – The topleft and bottomright annotations are required. **kwargs – Ignored.
Returns:	Annotations with reye and leye keys or None if it fails.
Return type:	dict

class bob.bio.face.annotator.BobIpMTCNN(**kwargs)¶

Bases: bob.bio.face.annotator.Base

Annotator using bob.ip.mtcnn

annotate(image, **kwargs)[source]¶

Annotates an image using bob.ip.mtcnn

Parameters:	image (numpy.array) – An RGB image in Bob format. **kwargs – Ignored.
Returns:	Annotations contain: (topleft, bottomright, leye, reye, nose, mouthleft, mouthright).
Return type:	dict

Preprocessors¶

class bob.bio.face.preprocessor.Base(dtype=None, color_channel='gray', **kwargs)¶

Bases: bob.bio.base.preprocessor.Preprocessor

Performs color space adaptations and data type corrections for the given image.

Parameters:

dtype : numpy.dtype or convertible or None: The data type that the resulting image will have.
color_channel : one of ('gray', 'red', 'gren', 'blue', 'rgb'): The specific color channel, which should be extracted from the image.

color_channel(image) → channel[source]¶

Returns the channel of the given image, which was selected in the constructor. Currently, gray, red, green and blue channels are supported.

Parameters:

image : 2D or 3D numpy.ndarray: The image to get the specified channel from.

Returns:

channel : 2D or 3D numpy.ndarray: The extracted color channel.

data_type(image) → image[source]¶

Converts the given image into the data type specified in the constructor of this class. If no data type was specified, or the image is None, no conversion is performed.

Parameters:

image : 2D or 3D numpy.ndarray: The image to convert.

Returns:

image : 2D or 3D numpy.ndarray: The image converted to the desired data type, if any.

class bob.bio.face.preprocessor.FaceCrop(cropped_image_size, cropped_positions, fixed_positions=None, mask_sigma=None, mask_neighbors=5, mask_seed=None, annotator=None, allow_upside_down_normalized_faces=False, **kwargs)¶

Bases: bob.bio.face.preprocessor.Base

Crops the face according to the given annotations.

This class is designed to perform a geometric normalization of the face based on the eye locations, using bob.ip.base.FaceEyesNorm. Usually, when executing the crop_face() function, the image and the eye locations have to be specified. There, the given image will be transformed such that the eye locations will be placed at specific locations in the resulting image. These locations, as well as the size of the cropped image, need to be specified in the constructor of this class, as cropped_positions and cropped_image_size.

Some image databases do not provide eye locations, but rather bounding boxes. This is not a problem at all. Simply define the coordinates, where you want your cropped_positions to be in the cropped image, by specifying the same keys in the dictionary that will be given as annotations to the crop_face() function.

Note

These locations can even be outside of the cropped image boundary, i.e., when the crop should be smaller than the annotated bounding boxes.

Sometimes, databases provide pre-cropped faces, where the eyes are located at (almost) the same position in all images. Usually, the cropping does not conform with the cropping that you like (i.e., image resolution is wrong, or too much background information). However, the database does not provide eye locations (since they are almost identical for all images). In that case, you can specify the fixed_positions in the constructor, which will be taken instead of the annotations inside the crop_face() function (in which case the annotations are ignored).

Sometimes, the crop of the face is outside of the original image boundaries. Usually, these pixels will simply be left black, resulting in sharp edges in the image. However, some feature extractors do not like these sharp edges. In this case, you can set the mask_sigma to copy pixels from the valid border of the image and add random noise (see bob.ip.base.extrapolate_mask()).

Parameters:

cropped_image_size ((int, int)) – The resolution of the cropped image, in order (HEIGHT,WIDTH); if not given, no face cropping will be performed
cropped_positions (dict) – The coordinates in the cropped image, where the annotated points should be put to. This parameter is a dictionary with usually two elements, e.g., {'reye':(RIGHT_EYE_Y, RIGHT_EYE_X) , 'leye':(LEFT_EYE_Y, LEFT_EYE_X)}. However, also other parameters, such as {'topleft' : ..., 'bottomright' : ...} are supported, as long as the annotations in the __call__ function are present.
fixed_positions (dict or None) – If specified, ignore the annotations from the database and use these fixed positions throughout.
mask_sigma (float or None) – Fill the area outside of image boundaries with random pixels from the border, by adding noise to the pixel values. To disable extrapolation, set this value to None. To disable adding random noise, set it to a negative value or 0.
mask_neighbors (int) – The number of neighbors used during mask extrapolation. See bob.ip.base.extrapolate_mask() for details.
mask_seed (int or None) – The random seed to apply for mask extrapolation.
allow_upside_down_normalized_faces (bool, optional) –
If False (default), a ValueError is raised when normalized faces are going to be upside down compared to input image. This allows you to catch wrong annotations in your database easily. If you are sure about your input, you can set this flag to True.

Warning

When run in parallel, the same random seed will be applied to all parallel processes. Hence, results of parallel execution will differ from the results in serial execution.
annotator (bob.bio.base.annotator.Annotator) – If provided, the annotator will be used if the required annotations are missing.
kwargs – Remaining keyword parameters passed to the Base constructor, such as color_channel or dtype.

crop_face(image, annotations=None)[source]¶

Crops the face. Executes the face cropping on the given image and returns the cropped version of it.

Parameters:	image (2D `numpy.ndarray`) – The face image to be processed. annotations (dict or `None`) – The annotations that fit to the given image. `None` is only accepted, when `fixed_positions` were specified in the constructor.
Returns:	face – The cropped face.
Return type:	2D `numpy.ndarray` (float)
Raises:	`ValueError` – If the annotations is None.

is_annotations_valid(annotations)[source]¶

class bob.bio.face.preprocessor.FaceDetect(face_cropper, cascade=None, use_flandmark=False, detection_overlap=0.2, distance=2, scale_base=0.9576032806985737, lowest_scale=0.125, **kwargs)¶

Bases: bob.bio.face.preprocessor.Base

Performs a face detection (and facial landmark localization) in the given image and crops the face.

This class is designed to perform a geometric normalization of the face based on the detected face. Face detection is performed using bob.ip.facedetect. Particularly, the function bob.ip.facedetect.detect_single_face() is executed, which will always return exactly one bounding box, even if the image contains more than one face, or no face at all. The speed of the face detector can be regulated using the cascade, distance` ``scale_base and lowest_scale parameters. The number of overlapping detected bounding boxes that should be joined can be selected by detection_overlap. Please see the documentation of bob.ip.facedetect for more details about these parameters.

Additionally, facial landmarks can be detected using the Python Bindings to the Flandmark Keypoint Localizer for Frontal Faces. If enabled using use_flandmark = True in the constructor, it is tried to obtain the facial landmarks inside the detected facial area. If landmarks are found, these are used to geometrically normalize the face. Otherwise, the eye locations are estimated based on the bounding box. This is also applied, when use_flandmark = False.

The face cropping itself is done by the given face_cropper. This cropper can either be an instance of FaceCrop (or any other class that provides a similar crop_face function), or it can be the resource name of a face cropper, such as 'face-crop-eyes'.

Parameters:

face_cropper : bob.bio.face.preprocessor.FaceCrop or str: The face cropper to be used to crop the detected face. Might be an instance of a FaceCrop or the name of a face cropper resource.
cascade : str or None: The file name, where a face detector cascade can be found. If None, the default cascade for frontal faces bob.ip.facedetect.default_cascade() is used.
use_flandmark : bool: If selected, bob.ip.flandmark.Flandmark is used to detect the eye locations. Otherwise, the eye locations are estimated based on the detected bounding box.
detection_overlap : float: See bob.ip.facedetect.detect_single_face().
distance : int: See the Sampling section in the Users Guide of bob.ip.facedetect.
scale_base : float: See the Sampling section in the Users Guide of bob.ip.facedetect.
lowest_scale : float: See the Sampling section in the Users Guide of bob.ip.facedetect.
kwargs: Remaining keyword parameters passed to the Base constructor, such as color_channel or dtype.

crop_face(image, annotations = None) → face[source]¶

Detects the face (and facial landmarks), and used the face_cropper given in the constructor to crop the face.

Parameters:

image : 2D or 3D numpy.ndarray: The face image to be processed.
annotations : any: Ignored.

Returns:

face : 2D or 3D numpy.ndarray (float): The detected and cropped face.

class bob.bio.face.preprocessor.HistogramEqualization(face_cropper, **kwargs)¶

Bases: bob.bio.face.preprocessor.Base

Crops the face (if desired) and performs histogram equalization to photometrically enhance the image.

Parameters:

face_cropper : str or bob.bio.face.preprocessor.FaceCrop or bob.bio.face.preprocessor.FaceDetect or None: The face image cropper that should be applied to the image. If None is selected, no face cropping is performed. Otherwise, the face cropper might be specified as a registered resource, a configuration file, or an instance of a preprocessor.

Note

The given class needs to contain a crop_face method.
kwargs: Remaining keyword parameters passed to the Base constructor, such as color_channel or dtype.

equalize_histogram(image) → equalized[source]¶

Performs the histogram equalization on the given image.

Parameters:

image : 2D numpy.ndarray: The image to berform histogram equalization with. The image will be transformed to type uint8 before computing the histogram.

Returns:

equalized : 2D numpy.ndarray (float): The photometrically enhanced image.

class bob.bio.face.preprocessor.INormLBP(face_cropper, radius=2, is_circular=True, compare_to_average=False, elbp_type='regular', **kwargs)¶

Bases: bob.bio.face.preprocessor.Base

Performs I-Norm LBP on the given image

class bob.bio.face.preprocessor.SelfQuotientImage(face_cropper, sigma=1.4142135623730951, **kwargs)¶

Bases: bob.bio.face.preprocessor.Base

Crops the face (if desired) and applies self quotient image algorithm [WLW04] to photometrically enhance the image.

Parameters:

face_cropper : str or bob.bio.face.preprocessor.FaceCrop or bob.bio.face.preprocessor.FaceDetect or None: The face image cropper that should be applied to the image. If None is selected, no face cropping is performed. Otherwise, the face cropper might be specified as a registered resource, a configuration file, or an instance of a preprocessor.

Note

The given class needs to contain a crop_face method.
sigma : float: Please refer to the [WLW04] original paper (see bob.ip.base.SelfQuotientImage documentation).
kwargs: Remaining keyword parameters passed to the Base constructor, such as color_channel or dtype.

class bob.bio.face.preprocessor.TanTriggs(face_cropper, gamma=0.2, sigma0=1, sigma1=2, size=5, threshold=10.0, alpha=0.1, **kwargs)¶

Bases: bob.bio.face.preprocessor.Base

Crops the face (if desired) and applies Tan&Triggs algorithm [TT10] to photometrically enhance the image.

Parameters:

face_cropper : str or bob.bio.face.preprocessor.FaceCrop or bob.bio.face.preprocessor.FaceDetect or None: The face image cropper that should be applied to the image. If None is selected, no face cropping is performed. Otherwise, the face cropper might be specified as a registered resource, a configuration file, or an instance of a preprocessor.

Note

The given class needs to contain a crop_face method.
gamma, sigma0, sigma1, size, threshold, alpha: Please refer to the [TT10] original paper (see bob.ip.base.TanTriggs documentation).
kwargs: Remaining keyword parameters passed to the Base constructor, such as color_channel or dtype.

Extractors¶

class bob.bio.face.extractor.DCTBlocks(block_size=12, block_overlap=11, number_of_dct_coefficients=45, normalize_blocks=True, normalize_dcts=True, auto_reduce_coefficients=False)¶

Bases: bob.bio.base.extractor.Extractor

Extracts Discrete Cosine Transform (DCT) features from (overlapping) image blocks. These features are based on the bob.ip.base.DCTFeatures class. The default parametrization is the one that performed best on the BANCA database in [WMM11].

Usually, these features are used in combination with the algorithms defined in bob.bio.gmm. However, you can try to use them with other algorithms.

Parameters:

block_size : int or (int, int): The size of the blocks that will be extracted. This parameter might be either a single integral value, or a pair (block_height, block_width) of integral values.
block_overlap : int or (int, int): The overlap of the blocks in vertical and horizontal direction. This parameter might be either a single integral value, or a pair (block_overlap_y, block_overlap_x) of integral values. It needs to be smaller than the block_size.
number_of_dct_coefficients : int: The number of DCT coefficients to use. The actual number will be one less since the first DCT coefficient (which should be 0, if normalization is used) will be removed.
normalize_blocks : bool: Normalize the values of the blocks to zero mean and unit standard deviation before extracting DCT coefficients.
normalize_dcts : bool: Normalize the values of the DCT components to zero mean and unit standard deviation. Default is True.

load(**kwargs)[source]¶

Loads the parameters required for feature extraction from the extractor file. This function usually is only useful in combination with the train() function. In this base class implementation, it does nothing.

Parameters:

extractor_file : str: The file to read the extractor from.

train(**kwargs)[source]¶

This function can be overwritten to train the feature extractor. If you do this, please also register the function by calling this base class constructor and enabling the training by requires_training = True.

Parameters:

training_data : [object] or [[object]]: A list of preprocessed data that can be used for training the extractor. Data will be provided in a single list, if split_training_features_by_client = False was specified in the constructor, otherwise the data will be split into lists, each of which contains the data of a single (training-)client.
extractor_file : str: The file to write. This file should be readable with the load() function.

class bob.bio.face.extractor.Eigenface(subspace_dimension)¶

Bases: bob.bio.base.extractor.Extractor

Performs a principal component analysis (PCA) on the given data.

This algorithm computes a PCA projection (bob.learn.linear.PCATrainer) on the given training images, and projects the images into face space. In opposition to bob.bio.base.algorithm.PCA, here the eigenfces are used as features, i.e., to apply advanced face recognition algorithms on top of them.

Parameters:

subspace_dimension : int or float: If specified as int, defines the number of eigenvectors used in the PCA projection matrix. If specified as float (between 0 and 1), the number of eigenvectors is calculated such that the given percentage of variance is kept.
kwargs : key=value pairs: A list of keyword arguments directly passed to the bob.bio.base.extractor.Extractor base class constructor.

load(extractor_file)[source]¶

Reads the PCA projection matrix from file.

Parameters:

extractor_file : str: An existing file, from which the PCA projection matrix are read.

train(training_images, extractor_file)[source]¶

Generates the PCA covariance matrix and writes it into the given extractor_file.

Beforehand, all images are turned into a 1D pixel vector.

Parameters:

training_images : [2D numpy.ndarray]: A list of 2D training images to train the PCA projection matrix with.
extractor_file : str: A writable file, into which the PCA projection matrix (as a bob.learn.linear.Machine) will be written.

class bob.bio.face.extractor.GridGraph(gabor_directions=8, gabor_scales=5, gabor_sigma=6.283185307179586, gabor_maximum_frequency=1.5707963267948966, gabor_frequency_step=0.7071067811865476, gabor_power_of_k=0, gabor_dc_free=True, normalize_gabor_jets=True, eyes=None, nodes_between_eyes=4, nodes_along_eyes=2, nodes_above_eyes=3, nodes_below_eyes=7, node_distance=None, first_node=None)¶

Bases: bob.bio.base.extractor.Extractor

Extracts Gabor jets in a grid structure [GHW12] using functionalities from bob.ip.gabor.

The grid can be either aligned to the eye locations (in which case the grid might be rotated), or a fixed grid graph can be extracted.

In the first case, the eye locations in the aligned image need to be provided. Additionally, the number of node between, along, above and below the eyes need to be specified.

In the second case, a regular grid graph is created, by specifying the distance between two nodes. Additionally, the coordinate of the first node can be provided, which otherwise is calculated to evenly fill the whole image with nodes.

Parameters:

gabor_directions, gabor_scales, gabor_sigma, gabor_maximum_frequency, gabor_frequency_step, gabor_power_of_k, gabor_dc_free: The parameters of the Gabor wavelet family, with its default values set as given in [WFK97]. Please refer to bob.ip.gabor.Transform for the documentation of these values.
normalize_gabor_jets : bool: Perform Gabor jet normalization during extraction?
eyes : dict or None: If specified, the grid setup will be aligned to the eye positions {‘reye’ : (re_y, re_x), ‘leye’ : (le_y, le_x)}. Otherwise a regular grid graph will be extracted.
nodes_between_eyes, nodes_along_eyes, nodes_above_eyes, nodes_below_eyes : int: Only used when eyes is not None. The number of nodes to be placed between, along, above or below the eyes. The final number of nodes will be: (above + below + 1) times (between + 2*along + 2).
node_distance : (int, int): Only used when eyes is None. The distance between two nodes in the regular grid graph.
first_node : (int, int) or None: Only used when eyes is None. If None, it is calculated automatically to equally cover the whole image.

load(**kwargs)[source]¶

Loads the parameters required for feature extraction from the extractor file. This function usually is only useful in combination with the train() function. In this base class implementation, it does nothing.

Parameters:

extractor_file : str: The file to read the extractor from.

read_feature(feature_file) → feature[source]¶

Reads the feature written by the write_feature() function from the given file.

Parameters:

feature_file : str or bob.io.base.HDF5File: The name of the file or the file opened for reading.

Returns:

feature : [bob.ip.gabor.Jet]: The list of Gabor jets read from file.

train(**kwargs)[source]¶

This function can be overwritten to train the feature extractor. If you do this, please also register the function by calling this base class constructor and enabling the training by requires_training = True.

Parameters:

training_data : [object] or [[object]]: A list of preprocessed data that can be used for training the extractor. Data will be provided in a single list, if split_training_features_by_client = False was specified in the constructor, otherwise the data will be split into lists, each of which contains the data of a single (training-)client.
extractor_file : str: The file to write. This file should be readable with the load() function.

write_feature(feature, feature_file)[source]¶

Writes the feature extracted by the __call__ function to the given file.

Parameters:

feature : [bob.ip.gabor.Jet]: The list of Gabor jets extracted from the image.
feature_file : str or bob.io.base.HDF5File: The name of the file or the file opened for writing.

class bob.bio.face.extractor.LGBPHS(block_size, block_overlap=0, gabor_directions=8, gabor_scales=5, gabor_sigma=6.283185307179586, gabor_maximum_frequency=1.5707963267948966, gabor_frequency_step=0.7071067811865476, gabor_power_of_k=0, gabor_dc_free=True, use_gabor_phases=False, lbp_radius=2, lbp_neighbor_count=8, lbp_uniform=True, lbp_circular=True, lbp_rotation_invariant=False, lbp_compare_to_average=False, lbp_add_average=False, sparse_histogram=False, split_histogram=None)¶

Bases: bob.bio.base.extractor.Extractor

Extracts Local Gabor Binary Pattern Histogram Sequences (LGBPHS) [ZSG05] from the images, using functionality from bob.ip.base and bob.ip.gabor.

The block size and the overlap of the blocks can be varied, as well as the parameters of the Gabor wavelet (bob.ip.gabor.Transform) and the LBP extractor (bob.ip.base.LBP).

Parameters:

block_size : int or (int, int): The size of the blocks that will be extracted. This parameter might be either a single integral value, or a pair (block_height, block_width) of integral values.
block_overlap : int or (int, int): The overlap of the blocks in vertical and horizontal direction. This parameter might be either a single integral value, or a pair (block_overlap_y, block_overlap_x) of integral values. It needs to be smaller than the block_size.
gabor_directions, gabor_scales, gabor_sigma, gabor_maximum_frequency, gabor_frequency_step, gabor_power_of_k, gabor_dc_free: The parameters of the Gabor wavelet family, with its default values set as given in [WFK97]. Please refer to bob.ip.gabor.Transform for the documentation of these values.
use_gabor_phases : bool: Extract also the Gabor phases (inline) and not only the absolute values. In this case, Extended LGBPHS features [ZSQ09] will be extracted.
lbp_radius, lbp_neighbor_count, lbp_uniform, lbp_circular, lbp_rotation_invariant, lbp_compare_to_average, lbp_add_average: The parameters of the LBP. Please see bob.ip.base.LBP for the documentation of these values.

Note

The default values are as given in [ZSG05] (the values of [ZSQ09] might differ).
sparse_histogram : bool: If specified, the histograms will be handled in a sparse way. This reduces the size of the extracted features, but the computation will take longer.

Note

Sparse histograms are only supported, when split_histogram = None.
split_histogram : one of ('blocks', 'wavelets', 'both') or None: Defines, how the histogram sequence is split. This could be interesting, if the histograms should be used in another way as simply concatenating them into a single histogram sequence (the default).

load(**kwargs)[source]¶

Loads the parameters required for feature extraction from the extractor file. This function usually is only useful in combination with the train() function. In this base class implementation, it does nothing.

Parameters:

extractor_file : str: The file to read the extractor from.

train(**kwargs)[source]¶

This function can be overwritten to train the feature extractor. If you do this, please also register the function by calling this base class constructor and enabling the training by requires_training = True.

Parameters:

training_data : [object] or [[object]]: A list of preprocessed data that can be used for training the extractor. Data will be provided in a single list, if split_training_features_by_client = False was specified in the constructor, otherwise the data will be split into lists, each of which contains the data of a single (training-)client.
extractor_file : str: The file to write. This file should be readable with the load() function.

Algorithms¶

class bob.bio.face.algorithm.GaborJet(gabor_jet_similarity_type, multiple_feature_scoring='max_jet', gabor_directions=8, gabor_scales=5, gabor_sigma=6.283185307179586, gabor_maximum_frequency=1.5707963267948966, gabor_frequency_step=0.7071067811865476, gabor_power_of_k=0, gabor_dc_free=True)¶

Bases: bob.bio.base.algorithm.Algorithm

Computes a comparison of lists of Gabor jets using a similarity function of bob.ip.gabor.Similarity.

The model enrollment simply stores all extracted Gabor jets for all enrollment features. By default (i.e., multiple_feature_scoring = 'max_jet'), the scoring uses an advanced local strategy. For each node, the similarity between the given probe jet and all model jets is computed, and only the highest value is kept. These values are finally averaged over all node positions. Other strategies can be obtained using a different multiple_feature_scoring.

Parameters:

gabor_jet_similarity_type : str:

The type of Gabor jet similarity to compute. Please refer to the documentation of bob.ip.gabor.Similarity for a list of possible values.

multiple_feature_scoring : str

How to fuse the local similarities into a single similarity value. Possible values are:

'average_model' : During enrollment, an average model is computed using functionality of bob.ip.gabor.
'average' : For each node, the average similarity is computed. Finally, the average of those similarities is returned.
'min_jet', 'max_jet', 'med_jet' : For each node, the minimum, maximum or median similarity is computed. Finally, the average of those similarities is returned.
'min_graph', 'max_graph', 'med_graph' : For each node, the average similarity is computed. Finally, the minimum, maximum or median of those similarities is returned.

gabor_directions, gabor_scales, gabor_sigma, gabor_maximum_frequency, gabor_frequency_step, gabor_power_of_k, gabor_dc_free

These parameters are required by the disparity-based Gabor jet similarity functions, see bob.ip.gabor.Similarity.. The default values are identical to the ones in the bob.bio.face.extractor.GridGraph. Please assure that this class and the bob.bio.face.extractor.GridGraph class get the same configuration, otherwise unexpected things might happen.

enroll(enroll_features) → model[source]¶

Enrolls the model using one of several strategies. Commonly, the bunch graph strategy [WFK97] is applied, by storing several Gabor jets for each node.

When multiple_feature_scoring = 'average_model', for each node the average bob.ip.gabor.Jet is computed. Otherwise, all enrollment jets are stored, grouped by node.

Parameters:

enroll_features : [[bob.ip.gabor.Jet]]: The list of enrollment features. Each sub-list contains a full graph.

Returns:

model : [[bob.ip.gabor.Jet]]: The enrolled model. Each sub-list contains a list of jets, which correspond to the same node. When multiple_feature_scoring = 'average_model' each sub-list contains a single bob.ip.gabor.Jet.

load_enroller(**kwargs)[source]¶

Loads the parameters required for model enrollment from file. This function usually is only useful in combination with the train_enroller() function. This function is always called after calling load_projector(). In this base class implementation, it does nothing.

Parameters:

enroller_file : str: The file to read the enroller from.

load_projector(**kwargs)[source]¶

Loads the parameters required for feature projection from file. This function usually is useful in combination with the train_projector() function. In this base class implementation, it does nothing.

Please register performs_projection = True in the constructor to enable this function.

Parameters:

projector_file : str: The file to read the projector from.

project(feature) → projected[source]¶

This function will project the given feature. It must be overwritten by derived classes, as soon as performs_projection = True was set in the constructor. It is assured that the load_projector() was called once before the project function is executed.

Parameters:

feature : object: The feature to be projected.

Returns:

projected : object: The projected features. Must be writable with the write_feature() function and readable with the read_feature() function.

read_feature(feature_file) → feature[source]¶

Reads the projected feature from file. In this base class implementation, it uses bob.io.base.load() to do that. If you have different format, please overwrite this function.

Please register performs_projection = True in the constructor to enable this function.

Parameters:

feature_file : str or bob.io.base.HDF5File: The file open for reading, or the file name to read from.

Returns:

feature : object: The feature that was read from file.

read_model(model_file) → model[source]¶

Reads the model written by the write_model() function from the given file.

Parameters:

model_file : str or bob.io.base.HDF5File: The name of the file or the file opened for reading.

Returns:

model : [[bob.ip.gabor.Jet]]: The list of Gabor jets read from file.

score(model, probe) → score[source]¶

Computes the score of the probe and the model using the desired Gabor jet similarity function and the desired score fusion strategy.

Parameters:

model : [[bob.ip.gabor.Jet]]: The model enrolled by the enroll() function.
probe : [bob.ip.gabor.Jet]: The probe, e.g., read by the bob.bio.face.extractor.GridGraph.read_feature() function.

Returns:

score : float: The fused similarity score.

score_for_multiple_models(models, probe) → score[source]¶

This function computes the score between the given model list and the given probe. In this base class implementation, it computes the scores for each model using the score() method, and fuses the scores using the fusion method specified in the constructor of this class. Usually this function is called from derived class score() functions.

Parameters:

models : [object]: A list of model objects.
probe : object: The probe object to compare the models with.

Returns:

score : float: The fused similarity between the given models and the probe.

score_for_multiple_probes(model, probes)[source]¶

score(model, probes) -> score

This function computes the score between the given model graph(s) and several given probe graphs. The same local scoring strategy as for several model jets is applied, but this time the local scoring strategy is applied between all graphs from the model and probes.

Parameters:

model : [[bob.ip.gabor.Jet]]: The model enrolled by the enroll() function. The sub-lists are groups by nodes.
probes : [[bob.ip.gabor.Jet]]: A list of probe graphs. The sub-lists are groups by graph.

Returns:

score : float: The fused similarity score.

train_enroller(**kwargs)[source]¶

This function can be overwritten to train the model enroller. If you do this, please also register the function by calling this base class constructor and enabling the training by require_enroller_training = True.

Parameters:

training_features : [object] or [[object]]: A list of extracted features that can be used for training the projector. Features will be split into lists, each of which contains the features of a single (training-)client.
enroller_file : str: The file to write. This file should be readable with the load_enroller() function.

train_projector(**kwargs)[source]¶

This function can be overwritten to train the feature projector. If you do this, please also register the function by calling this base class constructor and enabling the training by requires_projector_training = True.

Parameters:

training_features : [object] or [[object]]: A list of extracted features that can be used for training the projector. Features will be provided in a single list, if split_training_features_by_client = False was specified in the constructor, otherwise the features will be split into lists, each of which contains the features of a single (training-)client.
projector_file : str: The file to write. This file should be readable with the load_projector() function.

write_feature(**kwargs)[source]¶

Saves the given projected feature to a file with the given name. In this base class implementation:

If the given feature has a save attribute, it calls feature.save(bob.io.base.HDF5File(feature_file), 'w'). In this case, the given feature_file might be either a file name or a bob.io.base.HDF5File.
Otherwise, it uses bob.io.base.save() to do that.

If you have a different format, please overwrite this function.

Please register ‘performs_projection = True’ in the constructor to enable this function.

Parameters:

feature : object: A feature as returned by the project() function, which should be written.
feature_file : str or bob.io.base.HDF5File: The file open for writing, or the file name to write to.

write_model(model, model_file)[source]¶

Writes the model enrolled by the enroll() function to the given file.

Parameters:

model : [[bob.ip.gabor.Jet]]: The enrolled model.
model_file : str or bob.io.base.HDF5File: The name of the file or the file opened for writing.

class bob.bio.face.algorithm.Histogram(distance_function=<built-in function chi_square>, is_distance_function=True, multiple_probe_scoring='average')¶

Bases: bob.bio.base.algorithm.Algorithm

Computes the distance between histogram sequences.

Both sparse and non-sparse representations of histograms are supported. For enrollment, to date only the averaging of histograms is implemented.

Parameters:

distance_function : function: The function to be used to compare two histograms. This function should accept sparse histograms.
is_distance_function : bool: Is the given distance_function distance function (lower values are better) or a similarity function (higher values are better)?
multiple_probe_scoring : str or None: The way, scores are fused when multiple probes are available. See bob.bio.base.score_fusion_strategy() for possible values.

enroll(enroll_features) → model[source]¶

Enrolls a model by taking the average of all histograms.

enroll_features : [1D or 2D numpy.ndarray]: The histograms that should be averaged. Histograms can be specified sparse (2D) or non-sparse (1D)

Returns:

model : 1D or 2D numpy.ndarray: The averaged histogram, sparse (2D) or non-sparse (1D).

load_enroller(**kwargs)[source]¶

Loads the parameters required for model enrollment from file. This function usually is only useful in combination with the train_enroller() function. This function is always called after calling load_projector(). In this base class implementation, it does nothing.

Parameters:

enroller_file : str: The file to read the enroller from.

load_projector(**kwargs)[source]¶

Loads the parameters required for feature projection from file. This function usually is useful in combination with the train_projector() function. In this base class implementation, it does nothing.

Please register performs_projection = True in the constructor to enable this function.

Parameters:

projector_file : str: The file to read the projector from.

project(feature) → projected[source]¶

This function will project the given feature. It must be overwritten by derived classes, as soon as performs_projection = True was set in the constructor. It is assured that the load_projector() was called once before the project function is executed.

Parameters:

feature : object: The feature to be projected.

Returns:

projected : object: The projected features. Must be writable with the write_feature() function and readable with the read_feature() function.

read_feature(feature_file) → feature[source]¶

Reads the projected feature from file. In this base class implementation, it uses bob.io.base.load() to do that. If you have different format, please overwrite this function.

Please register performs_projection = True in the constructor to enable this function.

Parameters:

feature_file : str or bob.io.base.HDF5File: The file open for reading, or the file name to read from.

Returns:

feature : object: The feature that was read from file.

score(model, probe) → score[source]¶

Computes the score of the probe and the model using the desired histogram distance function. The resulting score is the negative distance, if is_distance_function = True. Both sparse and non-sparse models and probes are accepted, but their sparseness must agree.

Parameters:

model : 1D or 2D numpy.ndarray: The model enrolled by the enroll() function.
probe : 1D or 2D numpy.ndarray: The probe histograms, which can be specified sparse (2D) or non-sparse (1D)

Returns:

score : float: The resulting similarity score.

score_for_multiple_models(models, probe) → score[source]¶

This function computes the score between the given model list and the given probe. In this base class implementation, it computes the scores for each model using the score() method, and fuses the scores using the fusion method specified in the constructor of this class. Usually this function is called from derived class score() functions.

Parameters:

models : [object]: A list of model objects.
probe : object: The probe object to compare the models with.

Returns:

score : float: The fused similarity between the given models and the probe.

train_enroller(**kwargs)[source]¶

This function can be overwritten to train the model enroller. If you do this, please also register the function by calling this base class constructor and enabling the training by require_enroller_training = True.

Parameters:

training_features : [object] or [[object]]: A list of extracted features that can be used for training the projector. Features will be split into lists, each of which contains the features of a single (training-)client.
enroller_file : str: The file to write. This file should be readable with the load_enroller() function.

train_projector(**kwargs)[source]¶

This function can be overwritten to train the feature projector. If you do this, please also register the function by calling this base class constructor and enabling the training by requires_projector_training = True.

Parameters:

training_features : [object] or [[object]]: A list of extracted features that can be used for training the projector. Features will be provided in a single list, if split_training_features_by_client = False was specified in the constructor, otherwise the features will be split into lists, each of which contains the features of a single (training-)client.
projector_file : str: The file to write. This file should be readable with the load_projector() function.

write_feature(**kwargs)[source]¶

Saves the given projected feature to a file with the given name. In this base class implementation:

If the given feature has a save attribute, it calls feature.save(bob.io.base.HDF5File(feature_file), 'w'). In this case, the given feature_file might be either a file name or a bob.io.base.HDF5File.
Otherwise, it uses bob.io.base.save() to do that.

If you have a different format, please overwrite this function.

Please register ‘performs_projection = True’ in the constructor to enable this function.

Parameters:

feature : object: A feature as returned by the project() function, which should be written.
feature_file : str or bob.io.base.HDF5File: The file open for writing, or the file name to write to.