Merge branch 'csu' into 'master'

Archiving csu issue #29

Closes #29

See merge request !48

bob/bio/face/script/baselines.py

@@ -35,14 +35,6 @@ try:
 except:
   print("Could not load the GMM-based algorithms. Did you specify bob.bio.gmm in your config file?")
 
-try:
-  # try if the CSU extension is enabled
-  bob.bio.base.load_resource('lrpca', 'algorithm')
-  bob.bio.base.load_resource('lda-ir', 'algorithm')
-  all_algorithms += ['lrpca', 'lda-ir']
-except:
-  print("Could not load the algorithms from the CSU resources. Did you specify bob.bio.csu in your config file?")
-
 
 def command_line_arguments(command_line_parameters):
   """Defines the command line parameters that are accepted."""

doc/baselines.rst

@@ -125,25 +125,6 @@ Further algorithms are available, when the :ref:`bob.bio.gmm <bob.bio.gmm>` pack
 .. note::
   The ``ivector`` algorithm needs a lot of training data and fails on small databases such as the `AT&T database`_.
 
-
-Additionally, the following algorithms can be executed, when the :ref:`bob.bio.csu <bob.bio.csu>` package is installed.
-
-* ``lrpca``: In Local Region PCA [PBD11]_, the face is sub-divided into local regions and a PCA is performed for each local region.
-
-  - preprocessor : :py:class:`bob.bio.csu.preprocessor.LRPCA`
-  - feature : :py:class:`bob.bio.csu.extractor.LRPCA`
-  - algorithm : :py:class:`bob.bio.csu.algorithm.LRPCA`
-
-* ``lda-ir``: The LDA-IR (a.k.a. CohortLDA [LBP12]_) extracts color information from images after, and computes a PCA+LDA projection on two color layers.
-
-  - preprocessor : :py:class:`bob.bio.csu.preprocessor.LDAIR`
-  - feature : :py:class:`bob.bio.csu.extractor.LDAIR`
-  - algorithm : :py:class:`bob.bio.csu.algorithm.LDAIR`
-
-.. note::
-   The ``lrpca`` and ``lda-ir`` algorithms require hand-labeled eye locations.
-   Therefore, they can not be run on the default ``atnt`` database.
-
 .. _bob.bio.base.baseline_results:
 
 Baseline Results

doc/extra-intersphinx.txt

@@ -6,7 +6,6 @@ bob.ip.gabor
 bob.ip.base
 bob.bio.gmm
 bob.bio.video
-bob.bio.csu
 bob.bio.spear
 bob.db.lfw
 bob.ip.facedetect

doc/index.rst

@@ -21,7 +21,6 @@ Additionally, a set of baseline algorithms are defined, which integrate well wit
 
 * :ref:`bob.bio.gmm <bob.bio.gmm>` defines algorithms based on Gaussian mixture models
 * :ref:`bob.bio.video <bob.bio.video>` uses face recognition algorithms in video frames
-* :ref:`bob.bio.csu <bob.bio.csu>` provides wrapper classes of the `CSU Face Recognition Resources <http://www.cs.colostate.edu/facerec>`_ (only Python 2.7 compatible)
 
 For more detailed information about the structure of the ``bob.bio`` packages, please refer to the documentation of :ref:`bob.bio.base <bob.bio.base>`.
 Particularly, the installation of this and other ``bob.bio`` packages, please read the :ref:`bob.bio.base.installation`.

doc/links.rst

@@ -14,7 +14,5 @@
 .. _nist: http://www.nist.gov/itl/iad/ig/focs.cfm
 .. _pypi: http://pypi.python.org
 .. _sge: http://wiki.idiap.ch/linux/SunGridEngine
-.. _csu face recognition resources: http://www.cs.colostate.edu/facerec
-.. _xfacereclib.extension.csu: http://pypi.python.org/pypi/xfacereclib.extension.CSU
 .. _virtualbox: https://www.virtualbox.org
 .. _hdf5: http://www.hdfgroup.org/HDF5

doc/references.rst

@@ -9,8 +9,6 @@ References
 .. [TP91]    *M. Turk and A. Pentland*. **Eigenfaces for recognition**. Journal of Cognitive Neuroscience, 3(1):71-86, 1991.
 .. [ZKC98]  *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.
 .. [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.
-.. [PBD11]  *P.J. Phillips, J.R. Beveridge, B.A. Draper, G. Givens, A.J. O'Toole, D.S. Bolme, J. Dunlop, Y.M. Lui, H. Sahibzada and S. Weimer*. **An introduction to the good, the bad, & the ugly face recognition challenge problem**. Automatic face gesture recognition and workshops (FG 2011), pages 346-353. 2011.
-.. [LBP12]  *Y.M. Lui, D. Bolme, P.J. Phillips, J.R. Beveridge and B.A. Draper*. **Preliminary studies on the good, the bad, and the ugly face recognition challenge problem**. Computer vision and pattern recognition workshops (CVPRW), pages 9-16. 2012.
 .. [GHW12]   *M. Günther, D. Haufe and R.P. Würtz*. **Face recognition with disparity corrected Gabor phase differences**. In Artificial neural networks and machine learning, volume 7552 of Lecture Notes in Computer Science, pages 411-418. 9/2012.
 .. [ZSG05]  *W. Zhang, S. Shan, W. Gao, X. Chen and H. Zhang*. **Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition**. Computer Vision, IEEE International Conference on, 1:786-791, 2005.
 .. [MM09]    *C. McCool, S. Marcel*. **Parts-based face verification using local frequency bands**. In Advances in biometrics, volume 5558 of Lecture Notes in Computer Science. 2009.