Pipeline Simple: Introduction to biometric recognition in practice


Make sure the following conda packages are installed before following this tutorial:

conda install bob.bio.face

The bob.bio.face package contains the implementation of all face biometric databases. Same is true for other bob.bio packages.

To run biometric experiments, we provide a generic CLI command called bob bio pipeline. Such CLI command is an entry-point to several pipelines implemented in this package.

In our very first example, we’ve shown how to compare two samples using the bob bio compare-samples command, where the “biometric” algorithm is set with the argument --pipeline. A pipeline is an instance of bob.bio.base.pipelines.PipelineSimple.

Running a biometric experiment with Pipeline Simple

A bob bio pipeline simple command is available to run Biometrics experiments from the shell. Its options can be listed with:

$ bob bio pipeline simple --help

The command accepts a pipeline and a database to run the experiment.

Building your own Pipeline

The Pipeline Simple represents the simplest biometrics pipeline possible and for this reason, is the backbone for any biometric test in this library. It’s composed of:

Running the PipelineSimple will retrieve samples from a dataset and generate score files. It does not encompass the analysis of those scores (Error rates, ROC, DET). This can be done with other utilities of the bob.bio packages.


Following the structure of pipelines of scikit-learn, a Transformer is a class that must implement a Transformer.transform and a Transformer.fit() method. This class represents a simple operation that can be applied to data, like preprocessing of a sample or extraction of a feature vector from data.

A Transformer must implement the following methods:


This method takes data (X, N samples) as input and returns the corresponding transformed data. It is used for preprocessing and extraction.

Transformer.fit(X, y=None)

A Transformer can be trained with its Transformer.fit() method. For example, for Linear Discriminant Analysis (LDA), the algorithm must first be trained on data.

This method returns the instance of the class itself (self).


Not all Transformers need to be trained (via a fit method). For example, a preprocessing step that crops an image to a certain size does not require training. In this case, the Transformer.fit() method simply returns self. It is best to use sklearn.preprocessing.FunctionTransformer to create a transformer that does not require fit.

Below is an example implementing a very simple Transformer applying a custom function on each sample given as input.

from sklearn.base import TransformerMixin, BaseEstimator

class CustomTransformer(TransformerMixin, BaseEstimator):
    def transform(self, X):
        transformed_X = my_function(X)
        return transformed_X

    def fit(self, X, y=None):
        return self

    def _more_tags(self):
        return {"requires_fit": False}

or using sklearn.preprocessing.FunctionTransformer:

from sklearn.preprocessing import FunctionTransformer

def CustomTransformer(**kwargs):
    return FunctionTransformer(my_function, **kwargs)

Biometric Algorithm

A biometric algorithm takes as input features of one or more samples to create a template. Note that in many biometric databases, one template is generated from multiple samples such as several face images; This is true for both enrollment and probing templates. Once templates are generated, the biometric algorithm can compare the probe template to the enrollment template to generate a similarity score. These two operations are implemented in bob.bio.base.pipelines.BioAlgorithm.create_templates() and bob.bio.base.pipelines.BioAlgorithm.compare() methods.

A common example of a biometric algorithm class would compute the mean vector of a feature set (features of several samples to create one template) to generate a template, and would compare the scoring would be done by measuring the distance between the unknown mean vector and the enrolled mean vector.

BiometricAlgorithm.create_templates(list_of_feature_sets, enroll):

The bob.bio.base.pipelines.BioAlgorithm.create_templates() method takes a list of feature sets (a feature set is composed of features of several samples, e.g. images, that will be used to create one template) and returns a list of templates. The features are the output of the Transformer. The enroll parameter is a boolean that indicates if the templates are used for enrollment or for probing. Some algorithms may create a different template format for enrollment and for probing. For example, in the UBM-GMM algorithm ( see bob.bio.base.algorithm.GMM), the enrollment templates are adapted GMMs, while probing templates are statistics computed on the UBM.

BiometricAlgorithm.compare(enroll_templates, probe_templates)

The bob.bio.base.pipelines.BioAlgorithm.compare() method takes N enroll templates and M probe templates as input and returns an NxM matrix of similarity scores by comparing each probe template to each enroll template. The templates are the output of the create_templates method.

Here is a simple example of a custom bob.bio.base.pipelines.BioAlgorithm implementation that computes the mean of multiple samples to create a template, and measures the inverse of the distance as a similarity score.

import numpy as np
from bob.bio.base.pipelines import BioAlgorithm
class MyAlgorithm(BioAlgorithm):

    def create_templates(self, list_of_feature_sets, enroll):
        # you cannot call np.mean(list_of_feature_sets, axis=1) because the
        # number of features in each feature set may vary.
        return [np.mean(feature_set, axis=0) for feature_set in list_of_feature_sets]

    def compare(self, enroll_templates, probe_templates):
        scores = []
        for enroll_template in enroll_templates:
            for probe_template in probe_templates:
                similarity = 1 / np.linalg.norm(model - probe)
        scores = np.array(scores, dtype=float)
        return scores

Constructing the pipeline

As stated before, a pipeline is a series of Transformers and a BiometricAlgorithm chained together. In PipelineSimple, 3 sub-pipelines are defined: a training pipeline, an enrollment pipeline, and a scoring pipeline.

Data for training is passed to the Transformers *fit* method. Data for evaluation is going through the Transformers before being passed to the BiometricAlgorithm *enroll* or *score* methods.

Fig. 8 Example of a pipeline showing the sub-pipelines. The data of references is used for enrollment and the data of probes is used for scoring. Each subject’s data goes through the Transformer (or series of Transformers) before being given to BiometricAlgorithm.enroll() or BiometricAlgorithm.score().

Here is the creation of the pipeline combining the Transformer and the BioAlgorithm that we implemented earlier:

from sklearn.pipeline import make_pipeline
from bob.pipelines import wrap
from bob.bio.base.pipelines import PipelineSimple

# Instantiate the Transformer(s)
my_transformer = CustomTransformer()
# make it a sample transformer (explained later)
my_transformer = wrap(["sample"], my_transformer)

# Chain the Transformers together
transformer = make_pipeline(
    # Add more transformers here if needed

# Instantiate the BioAlgorithm
bio_algorithm = CustomDistance()

# Assemble the PipelineSimple and execute
pipeline = PipelineSimple(transformer, bio_algorithm)

Minimal example of a PipelineSimple experiment

To run a minimal example, let’s use the ATNT faces database and execute this pipeline. The ATNT database is included in this package.


Usually, you need to download the files of each database manually yourself. We do not and cannot provide a script that downloads a biometric database automatically due to legal restrictions.

For each database, you need to configure Bob to specify the location of its files. To do so for e.g. the MOBIO database (no need for ATNT database), run the following command:

$ bob config set bob.db.mobio.directory ~/datasets/mobio


Since the original files of the ATNT database is included in this package, you can skip the step above.

For more information, see Global Configuration System.

Find below a complete file containing a Transformer, a Biometric Algorithm, and the construction of the pipeline:

import numpy as np

from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.utils import check_array

from bob.bio.base.pipelines import BioAlgorithm, PipelineSimple
from bob.pipelines import wrap

# Transformers #
pca = PCA(n_components=0.95)

# the images are in shape of Nx112x92, we want to flatten to Nx10304 them so we can train a PCA on them.
def flatten(images):
    images = check_array(images, allow_nd=True)
    new_shape = [images.shape[0], -1]
    return np.reshape(images, new_shape)

flatten_transformer = FunctionTransformer(flatten, validate=False)

# Chain the Transformers together
transformer = make_pipeline(flatten_transformer, pca)

# All transformers must be sample transformers
transformer = wrap(["sample"], transformer)

# Implementation of the BioAlgorithm #
# A generic implementation is available in:
# from bob.bio.base.algorithm import Distance
class EuclideanDistance(BioAlgorithm):
    def create_templates(self, list_of_feature_sets, enroll):
        # list_of_feature_sets is a list of lists of features with the shape of Nx?xD.
        # The second dimension can be variable in list_of_feature_sets depening on the
        # datbase protocol. Hence, you cannot call:
        # list_of_feature_sets = np.array(list_of_feature_sets)
        templates = []
        for feature_set in list_of_feature_sets:
            # template is a D dimensional vector
            template = np.mean(feature_set, axis=0)
        # templates is an NxD dimensional array
        return templates

    def compare(self, enroll_templates, probe_templates):
        # enroll_templates is an NxD dimensional array
        # probe_templates is an MxD dimensional array
        # scores will be an NxM dimensional array
        scores = []
        for model in enroll_templates:
            for probe in probe_templates:
                similarity = 1 / np.linalg.norm(model - probe)
        scores = np.array(scores, dtype=float)
        return scores

bio_algorithm = EuclideanDistance()

# Creation of the pipeline #
# `pipeline` will be used by the `bob bio pipeline simple` command
pipeline = PipelineSimple(transformer, bio_algorithm)

# you can also specify the other options in this file:
database = "atnt"
output = "results"

To run the simple example above, save that code in a file my_pipeline.py and enter this command in a terminal:

$ bob bio pipeline simple -vv /path/to/my_pipeline.py

This will create a file results/scores-dev containing the distance between each pair of probe and reference template.


You can specify all options in one .py file above when providing the config file as an argument, like the example. To create a sample config file, run:

$ bob bio pipeline simple --dump-config sample_config.py

Structure of a pipeline

In a serious scenario with more complex and longer implementations, you should separate the definition of Transformers and BioAlgorithm in different files that can be swapped more easily.

bob.bio packages also provide commonly used pipelines and databases that you can use. You can list them with either of the following commands:

$ bob bio pipeline simple --help
$ bob bio pipeline simple --dump-config sample_config.py

For example, to test the iresnet100 pipeline on the ATNT database, run:

$ bob bio pipeline simple -vv atnt iresnet100

The command above is equivalent to the following command:

$ bob bio pipeline simple -vv \
  bob.bio.base.config.database.atnt \

This information can be obtained by either inspecting the setup.py of the relevant pacakge or using the following code snippet:

import pkg_resources

for entry_point in pkg_resources.iter_entry_points("bob.bio.config"):
    if entry_point.name not in ("atnt", "iresnet100"):

See Extending packages as frameworks for more information.


Many pipelines depend on the fact that you run them like: bob bio pipeline simple -vv <database> <pipeline> where no --database and --pipeline is used and the database is specified before the pipeline.