.. vim: set fileencoding=utf-8 : .. author: Manuel Günther .. author: Tiago de Freitas Pereira .. date: Thu Sep 20 11:58:57 CEST 2012 .. _bob.bio.base: ===================================== Resources for biometric experiments ===================================== ``bob.bio.base`` provides open-source tools to run comparable and reproducible biometric recognition experiments. It covers the following biometrics traits: * Face Biometrics: `bob.bio.face `__ * Vein Biometrics: `bob.bio.vein `__ * Speaker Biometrics: `bob.bio.spear `__ Get Started ============ This package defines the structure of biometric experiments. After installing the necessary environment, you can try out a simple comparison between two (or more) samples using a face recognition algorithm from `bob.bio.face `__, for example. Run the following command:: $ bob bio compare-samples --pipeline facenet-sanderberg me.png not_me.png The ``--pipeline`` option indicates which algorithm should be used to compare the pictures. The list of all available pipelines is available in the help text of the ``--pipeline`` option:: $ bob bio compare-samples --help ... Options: -p, --pipeline CUSTOM Vanilla biometrics pipeline composed of a scikit- learn Pipeline and a BioAlgorithm Can be a ``bob.bio.pipeline`` entry point, a module name, or a path to a Python file which contains a variable named `pipeline`.Available entry points are: ..., facenet-sanderberg, ... Of course, with that command, you can run every possible biometric experiment by *headbutting* the problem and executing everything by hand. Or you could use the tools that we offer here to set up an :ref:`experimentation pipeline `, structure your data within a :ref:`database interface ` and run a whole experiment in one swoop. Citing our Publications ======================= 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 biometrics_intro pipeline_simple_intro pipeline_simple_features pipeline_score_norm annotators legacy vulnerability_analysis Reference Manual ================ .. toctree:: :maxdepth: 2 py_api References ========== .. [Auckenthaler2000] Auckenthaler, Roland, Michael Carey, and Harvey Lloyd-Thomas. "Score normalization for text-independent speaker verification systems." Digital Signal Processing 10.1 (2000): 42-54. .. [Mariethoz2005] Mariethoz, Johnny, and Samy Bengio. "A unified framework for score normalization techniques applied to text-independent speaker verification." IEEE signal processing letters 12.7 (2005): 532-535. .. [Mandasari2014] `Mandasari, Miranti Indar, et al. "Score calibration in face recognition." Iet Biometrics 3.4 (2014): 246-256.` Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. include:: links.rst