{ "info": { "author": "Laurent El Shafey", "author_email": "Laurent.El-Shafey@idiap.ch", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Natural Language :: English", "Programming Language :: Python", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "Experiments of Laurent El Shafey's Ph.D. Thesis\n===============================================\n\nThis package contains scripts to reproduce the experiments of my Ph.D. thesis at Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL).\nIt was developed when I was working in the `Biometrics group `_ at the `Idiap Research Institute `_::\n\n @phdthesis{ElShafey_EPFL2014,\n title = {Scalable Probabilistic Models for Face and Speaker Recognition},\n author = {Laurent El Shafey},\n month = {April},\n year = {2014},\n school = {Ecole Polytechnique F{\\'e}d{\\'e}rale de Lausanne (EPFL)},\n url = {http://publications.idiap.ch/index.php/publications/show/2830},\n }\n\nIn particular, this package provides instructions to combine code (see Bob_, which contains the implementations of machine learning algorithms and signal processing tools) and data to generate the plots depicted in my thesis.\n\n\nInstallation\n------------\n\nTo download the xbob.thesis.elshafey2014 package, please go to http://pypi.python.org/pypi/xbob.thesis.elshafey2014, click on the **download** button and extract the .zip file to a folder of your choice.\n\nThe xbob.thesis.elshafey2014 is a satellite package of the free signal processing and machine learning library Bob_, and some of its algorithms rely on the `CSU Face Recognition Resources`_.\nThese two dependencies have to be downloaded manually, as explained in the following.\n\n\nBob\n...\n\nYou will need a copy of Bob in version 1.2.2 to run the algorithms.\nPlease download Bob_ from its webpage.\nAfter downloading, you should go to the console and write::\n\n $ python bootstrap.py\n $ bin/buildout\n\nThis will download all required dependencies and install them locally.\nIf you don't want all the database packages to be downloaded, please remove the xbob.db.[database] lines from the ``eggs`` section of the file **buildout.cfg** in the main directory before calling the three commands above.\n\n\nThe CSU Face Recognition Resources\n..................................\n\nTwo open source face recognition algorithms are provided by the `CSU Face Recognition Resources`_, namely the LRPCA and the LDA-IR (a.k.a. CohortLDA) algorithm.\nFor these algorithms, optional wrapper classes are provided in the xfacereclib.extension.CSU_ satellite package.\nBy default, this package is disabled.\nTo enable them, please call::\n\n $ bin/buildout -c buildout-with-csu.cfg\n\nafter downloading and patching the CSU resources, and updating the ``sources-dir`` in the **buildout-with-csu.cfg** file -- as explained in xfacereclib.extension.CSU_.\n\n\nDatabases\n.........\n\nExperiments are conducted on several databases.\nThey should be downloaded and extracted manually to be able to reproduce the plots.\n\n- BANCA [http://www.ee.surrey.ac.uk/CVSSP/banca]\n- AR face database [http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html]\n- Face Recognition Grand Challenge version 2 [http://www.nist.gov/itl/iad/ig/frgc.cfm]\n- The Good, The Bad and the Ugly [http://www.nist.gov/itl/iad/ig/focs.cfm]\n- Labeled Faces in the Wild [http://vis-www.cs.umass.edu/lfw] (images aligned with funneling [http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz] and annotations [http://www.idiap.ch/resource/biometric/data/LFW-Annotations.tar.gz])\n- Multi-PIE [http://www.multipie.org]\n- MOBIO [http://www.idiap.ch/dataset/mobio] (images, audio data and image annotations)\n- CAS-PEAL [http://www.jdl.ac.cn/peal/index.html]\n- NIST Speaker Recognition Evaluation 2012 [http://www.nist.gov/itl/iad/mig/sre12.cfm] (Additional data distributed by `LDC `_ are required for training and development purposes [see https://pypi.python.org/pypi/xbob.db.nist_sre12 to preprocess the data])\n\nOnce you have installed the databases, you should set the path to raw data into some configuration files.\nFor instance, for running experiments on the BANCA database, you should set the variable 'banca_directory'\nin the file `banca.py `_ to your directory that \ncontains the images of this database.\nThis is explained in more details in the complete documentation.\n\n\nRunning experiments\n-------------------\n\nIf you have set up everything mentioned above, you are ready to run the recognition experiments.\n\nThis process is split into two different steps::\n\n 1. Generation of raw scores from raw data (image/audio files [and possibly annotations]) of the databases\n\n 2. Generation of plots from these raw scores\n\nThere is a single exception with the plots generated on the M-iris synthetic dataset.\nIn this case, the experiments does not require any external data and can be reproduced in one step.\nIn practice, the first step has high computational requirements, which depend on the database considered.\n\n\nRead further\n------------\n\nThere are several file links in the documentation, which won't work in the online documentation.\nTo generate the documentation locally, type::\n\n $ bin/sphinx-build docs sphinx\n $ firefox sphinx/index.html\n\nand read further instructions on how to use this package.\n\n\nCite our paper\n--------------\n\nIf you use this package in any of your experiments, please cite the following paper::\n\n @phdthesis{ElShafey_EPFL2014,\n title = {Scalable Probabilistic Models for Face and Speaker Recognition},\n author = {Laurent El Shafey},\n month = {April},\n year = {2014},\n school = {Ecole Polytechnique F{\\'e}d{\\'e}rale de Lausanne (EPFL)},\n url = {http://publications.idiap.ch/index.php/publications/show/2830},\n }\n\n\nProblems\n--------\n\nIn case of problems, please contact me (Laurent El Shafey).\n\nIf you are facing technical issues to be able to run the scripts \nof this package, you can send a message on the `Bob's mailing list\n`_.\n\nPlease follow `these guidelines \n`_\nwhen (or even better before) reporting any bug.\n\n\n.. _bob: http://www.idiap.ch/software/bob\n.. _csu face recognition resources: http://www.cs.colostate.edu/facerec\n.. _xfacereclib.extension.csu: http://pypi.python.org/pypi/xfacereclib.extension.CSU", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://pypi.python.org/pypi/xbob.thesis.elshafey2014", "keywords": "bob,xbob,face recognition,speaker recognition,bimodal recognition,facereclib,xbob.spkrec,GMM,ISV,JFA,TV,i-vector,PLDA,EPFL,Idiap", "license": "GPLv3", "maintainer": null, "maintainer_email": null, "name": "xbob.thesis.elshafey2014", "package_url": "https://pypi.org/project/xbob.thesis.elshafey2014/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/xbob.thesis.elshafey2014/", "project_urls": { "Download": "UNKNOWN", "Homepage": "https://pypi.python.org/pypi/xbob.thesis.elshafey2014" }, "release_url": "https://pypi.org/project/xbob.thesis.elshafey2014/0.0.1a0/", "requires_dist": null, "requires_python": null, "summary": "Experiments of Laurent El Shafey's Ph.D. thesis", "version": "0.0.1a0" }, "last_serial": 1074282, "releases": { "0.0.1a0": [ { "comment_text": "", "digests": { "md5": "eff00c9729a1bf14dad676c3ad9794f1", "sha256": "6694fc4ee795337f87e5862ded54656ccd763b94e2771e20c69b56cba055776d" }, "downloads": -1, "filename": "xbob.thesis.elshafey2014-0.0.1a0.zip", "has_sig": false, "md5_digest": "eff00c9729a1bf14dad676c3ad9794f1", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 234482, "upload_time": "2014-04-28T14:14:38", "url": "https://files.pythonhosted.org/packages/cf/b3/16de072b4aac1df2c5dc3ca3195279203799bf47b046520318572f320b2d/xbob.thesis.elshafey2014-0.0.1a0.zip" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "eff00c9729a1bf14dad676c3ad9794f1", "sha256": "6694fc4ee795337f87e5862ded54656ccd763b94e2771e20c69b56cba055776d" }, "downloads": -1, "filename": "xbob.thesis.elshafey2014-0.0.1a0.zip", "has_sig": false, "md5_digest": "eff00c9729a1bf14dad676c3ad9794f1", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 234482, "upload_time": "2014-04-28T14:14:38", "url": "https://files.pythonhosted.org/packages/cf/b3/16de072b4aac1df2c5dc3ca3195279203799bf47b046520318572f320b2d/xbob.thesis.elshafey2014-0.0.1a0.zip" } ] }