.. vim: set fileencoding=utf-8 : .. author: Manuel Günther .. date: Thu Sep 20 11:58:57 CEST 2012 .. _bob.bio.base: =========================================== Running Biometric Recognition Experiments =========================================== The ``bob.bio`` packages provide open source tools to run comparable and reproducible biometric recognition experiments. To design a biometric recognition experiment, you must choose: * A database to use for the raw biometric data and a protocol that defines how to use that data, * A data preprocessing algorithm to clean up the raw biometric data, * A feature extractor to extract the desired type of features from the preprocessed data, * A biometric matching algorithm, * An evaluation method to make sense of the matching scores. The ``bob.bio`` packages contain several implementations of each of the above steps, so you can either choose from the existing methods or use your own. .. note:: The ``bob.bio`` packages are derived from the former `FaceRecLib `__, which is herewith outdated. The :py:mod:`bob.bio.base` package includes the basic definition of a biometric recognition experiment, as well as a generic script, which can execute the full biometric experiment in a single command line. Changing the employed tools, such as the database, protocol, preprocessor, feature extractor or matching algorithm is as simple as changing a parameter in a configuration file or on the command line. The implementation of (most of) the tools is separated into other packages in the ``bob.bio`` namespace. All of these packages can be easily combined. Here is a growing list of derived packages: * :ref:`bob.bio.spear ` Tools to run speaker recognition experiments, including voice activity detection, Cepstral feature extraction, and speaker databases * :ref:`bob.bio.vein ` Tools to run vein recognition experiments, such as finger RoI detection, image binarization and template matching, and access to multiple vein image databases * :ref:`bob.bio.face ` Tools to run face recognition experiments, such as face detection, facial feature extraction and comparison, and face image databases * :ref:`bob.bio.video ` An extension of face recognition algorithms to run on video data, and the according video databases * :ref:`bob.bio.gmm ` Algorithms based on Gaussian Mixture Modeling (GMM) such as Inter-Session Variability modeling (ISV) or Total Variability modeling (TV, aka. I-Vector) [Pri07]_ and [ESM+13]_. If you run biometric recognition experiments using the bob.bio framework, please cite at least one of the following in your scientific publication: .. code-block:: tex @inbook{guenther2016face, chapter = {Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey}, author = {G\"unther, Manuel and El Shafey, Laurent and Marcel, S\'ebastien}, editor = {Bourlai, Thirimachos}, title = {Face Recognition Across the Imaging Spectrum}, edition = {1}, year = {2016}, month = feb, publisher = {Springer} } @inproceedings{guenther2012facereclib, title = {An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms}, author = {G\"unther, Manuel and Wallace, Roy and Marcel, S\'ebastien}, editor = {Fusiello, Andrea and Murino, Vittorio and Cucchiara, Rita}, booktitle = {European Conference on Computer Vision (ECCV) Workshops and Demonstrations}, series = {Lecture Notes in Computer Science}, volume = {7585}, year = {2012}, month = oct, pages = {547-556}, publisher = {Springer}, } Users Guide =========== .. toctree:: :maxdepth: 2 installation struct_bio_rec_sys experiments implementation filelist-guide more annotations Reference Manual ================ .. toctree:: :maxdepth: 2 implemented py_api References ========== .. [TP91] *M. Turk and A. Pentland*. **Eigenfaces for recognition**. Journal of Cognitive Neuroscience, 3(1):71-86, 1991. .. [ZKC+98] *W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets and J. Weng*. **Discriminant analysis of principal components for face recognition**, pages 73-85. Springer Verlag Berlin, 1998. .. [Pri07] *S. J. D. Prince*. **Probabilistic linear discriminant analysis for inferences about identity**. Proceedings of the International Conference on Computer Vision. 2007. .. [ESM+13] *L. El Shafey, Chris McCool, Roy Wallace and Sébastien Marcel*. **A scalable formulation of probabilistic linear discriminant analysis: applied to face recognition**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7):1788-1794, 7/2013. .. [MWP98] *B. Moghaddam, W. Wahid and A. Pentland*. **Beyond eigenfaces: probabilistic matching for face recognition**. IEEE International Conference on Automatic Face and Gesture Recognition, pages 30-35. 1998. .. [GW09] *M. Günther and R.P. Würtz*. **Face detection and recognition using maximum likelihood classifiers on Gabor graphs**. International Journal of Pattern Recognition and Artificial Intelligence, 23(3):433-461, 2009. ToDo-List ========= This documentation is still under development. Here is a list of things that needs to be done: .. todolist:: Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. include:: links.rst