xbob.paper.tpami2013 0.2.0a0

This package contains scripts that shows how to use the implementation of the scalable formulation of Probabilistic Linear Discriminant Analysis (PLDA), integrated into bob, as well as how to reproduce experiments of the article mentioned below.

If you use this package and/or its results, please cite the following publications:

1. The original paper with the scalable formulation of PLDA explained in details:

   @article{ElShafey_TPAMI_2013,
     author = {El Shafey, Laurent and McCool, Chris and Wallace, Roy and Marcel, S{\'{e}}bastien},
     title = {A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition},
     year = {2013},
     month = jul,
     journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     volume = {35},
     number = {7},
     pages = {1788-1794},
     doi = {10.1109/TPAMI.2013.38},
     pdf = {http://publications.idiap.ch/downloads/papers/2013/ElShafey_TPAMI_2013.pdf}
   }

2. Bob as the core framework used to run the experiments:

   @inproceedings{Anjos_ACMMM_2012,
     author = {A. Anjos and L. El Shafey and R. Wallace and M. G\"unther and C. McCool and S. Marcel},
     title = {Bob: a free signal processing and machine learning toolbox for researchers},
     year = {2012},
     month = oct,
     booktitle = {20th ACM Conference on Multimedia Systems (ACMMM), Nara, Japan},
     publisher = {ACM Press},
     url = {http://publications.idiap.ch/downloads/papers/2012/Anjos_Bob_ACMMM12.pdf},
   }

3. If you decide to use the Multi-PIE database, you should also mention the following paper, where it is introduced:

   @article{Gross_IVC_2010,
    author = {Gross, Ralph and Matthews, Iain and Cohn, Jeffrey and Kanade, Takeo and Baker, Simon},
    title = {Multi-PIE},
    journal = {Image and Vision Computing},
    year = {2010},
    month = may,
    volume = {28},
    number = {5},
    issn = {0262-8856},
    pages = {807--813},
    numpages = {7},
    doi = {10.1016/j.imavis.2009.08.002},
    url = {http://dx.doi.org/10.1016/j.imavis.2009.08.002},
    acmid = {1747071},
   }

4. If you only use the Multi-PIE annotations, you should cite the following paper since annotations were made for the experiments of this work:

   @article{ElShafey_TPAMI_2013,
     author = {El Shafey, Laurent and McCool, Chris and Wallace, Roy and Marcel, S{\'{e}}bastien},
     title = {A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition},
     year = {2013},
     month = jul,
     journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     volume = {35},
     number = {7},
     pages = {1788-1794},
     doi = {10.1109/TPAMI.2013.38},
     pdf = {http://publications.idiap.ch/downloads/papers/2013/ElShafey_TPAMI_2013.pdf}
   }

$ pip install xbob.paper.tpami2013

$ easy_install xbob.paper.tpami2013

$ wget http://pypi.python.org/packages/source/x/xbob.paper.tpami2013/xbob.paper.tpami2013-0.2.0a0.zip
$ unzip xbob.paper.tpami2013-0.2.0a0.zip
$ cd xbob.paper.tpami2013

$ python bootstrap.py
$ ./bin/buildout

$ ./bin/plda_example_iris.py --output-img plda_example_iris.png

$ ./bin/lbph_features.py --image-dir /PATH/TO/MULTIPIE/IMAGES --annotation-dir /PATH/TO/MULTIPIE/ANNOTATIONS --output-dir /PATH/TO/OUTPUT_DIR/

$ ./bin/pca_features.py --output-dir /PATH/TO/OUTPUT_DIR/

$ ./bin/toolchain_plda.py --output-dir /PATH/TO/OUTPUT_DIR/

$ ./bin/bob_compute_perf.py -d /PATH/TO/OUTPUT_DIR/U/plda/scores/scores-dev -t /PATH/TO/OUTPUT_DIR/U/plda/scores/scores-eval -x

$ ./bin/experiment_plda_subworld.py --output-dir /PATH/TO/OUTPUT_DIR/
$ ./bin/plot_figure2.py --output-dir /PATH/TO/OUTPUT_DIR/