diff --git a/bob/bio/face/script/baselines.py b/bob/bio/face/script/baselines.py
index 0b7219130866cde61a9d4e9684a5aa07d3cca209..b6f2a4710d6f0f7c061565d4f267ec1afd3287c0 100755
--- a/bob/bio/face/script/baselines.py
+++ b/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."""
diff --git a/doc/baselines.rst b/doc/baselines.rst
index 3f60912f0fcd1f03b9a2bb3d1163402f37f24924..6df5fdbe599671b36bef2bdfda781d646c22bb17 100644
--- a/doc/baselines.rst
+++ b/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
diff --git a/doc/extra-intersphinx.txt b/doc/extra-intersphinx.txt
index c82f95f8fd7a78b61d0116246d7a6f2ff9a69a65..bf1097ebbeb8c2be2f37cafc1f3f8a6e4b937fd1 100644
--- a/doc/extra-intersphinx.txt
+++ b/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
diff --git a/doc/index.rst b/doc/index.rst
index 361114f346038cc84fba6ef2377f65e823ac6a9c..7a6cb6865d0044602032e7649b0ff0b228ebce2e 100644
--- a/doc/index.rst
+++ b/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`.
diff --git a/doc/links.rst b/doc/links.rst
index b055ac96611f9b7a16225ab016cfb5bac080d119..f4b19432bcd96a26a5d5b4cb666698191953ec17 100644
--- a/doc/links.rst
+++ b/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
diff --git a/doc/references.rst b/doc/references.rst
index c1cfb2c8065d144dcb8318475cd38b503abbb989..22fb8ffc61e6e09e7706923be6d9501f3eb254d5 100644
--- a/doc/references.rst
+++ b/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.