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xbob.paper.tpami2013 0.2.0a2

Example on how to use the scalable implementation of PLDA and how to reproduce experiments of the article

Latest Version: 1.0.0

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. It is implemented and maintained via github

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}
    }
    

Installation

Note

If you are reading this page through our GitHub portal and not through PyPI, note the development tip of the package may not be stable or become unstable in a matter of moments.

Go to http://pypi.python.org/pypi/xbob.paper.tpami2013 to download the latest stable version of this package.

There are two options you can follow to get this package installed and operational on your computer: you can use automatic installers like pip (or easy_install) or manually download, unpack and use zc.buildout to create a virtual work environment just for this package.

Using an automatic installer

Using pip is the easiest (shell commands are marked with a $ signal):

$ pip install xbob.paper.tpami2013

You can also do the same with easy_install:

$ easy_install xbob.paper.tpami2013

This will download and install this package plus any other required dependencies. It will also verify if the version of Bob you have installed is compatible.

This scheme works well with virtual environments by virtualenv or if you have root access to your machine. Otherwise, we recommend you use the next option.

Using zc.buildout

Download the latest version of this package from PyPI and unpack it in your working area:

$ 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

The installation of the toolkit itself uses buildout. You don't need to understand its inner workings to use this package. Here is a recipe to get you started:

$ python bootstrap.py
$ ./bin/buildout

These two commands should download and install all non-installed dependencies and get you a fully operational test and development environment.

Please note that this package also requires that bob (>= 1.2.0) is installed.

Note

The python shell used in the first line of the previous command set determines the python interpreter that will be used for all scripts developed inside this package. Because this package makes use of Bob, you must make sure that the bootstrap.py script is called with the same interpreter used to build Bob, or unexpected problems might occur.

If Bob is installed by the administrator of your system, it is safe to consider it uses the default python interpreter. In this case, the above 3 command lines should work as expected. If you have Bob installed somewhere else on a private directory, edit the file buildout.cfg before running ./bin/buildout. Find the section named buildout and edit or add the line prefixes to point to the directory where Bob is installed or built. For example:

[buildout]
...
prefixes=/home/laurent/work/bob/build

PLDA tutorial

The following example consists of a simple script, that makes use of PLDA modeling on the Fisher's iris dataset. It performs the following tasks:

  1. Train a PLDA model using the first two classes of the dataset
  2. Enroll a class-specific PLDA model for the third class of the dataset
  3. Compute (verification) scores for both positive and negative samples
  4. Plot the distribution of the scores and save it into a file

To run this simple example, you just need to execute the following command:

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

Reproducing experiments

It is currently possible to reproduce the experiments on Multi-PIE using the PLDA algorithm. In particular, the Figure 2 of the article can be easily reproduced, by following the steps described below, as well as the HTER reported on Table 3.

The experiments that make use of the LFW database may be integrated later on in this package.

Note for Grid Users

At Idiap, we use the powerful Sun Grid Engine (SGE) to parallelize our job submissions as much as we can. At the Biometrics group, we have developed a little toolbox <http://pypi.python.org/pypi/gridtk> that can submit and manage jobs at the Idiap computing grid through SGE.

The following sections will explain how to reproduce the paper results in single (non-gridified) jobs. If you are at Idiap, you could run the following commands on the SGE infrastructure, by applying the '--grid' flag to any command. This may also work on other locations with an SGE infrastructure, but will likely require some configuration changes in the gridtk utility.

Multi-PIE dataset

Getting the data

You first need to buy and download the Multi-PIE database:
<http://multipie.org/>
and to download the annotations available here:
<http://www.idiap.ch/resource/biometric/>

Feature extraction

The following command will extract LBP histograms features. You should set the paths to the data according to your own environment:

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

Note

The output directory /PATH/TO/MULTIPIE/OUTPUT_DIR/ is a base directory for the output of all experiments on Multi-PIE. Make sure to use the same directory for all the experiments below, otherwise the following commands might not work as expected. You can look at the options of the scripts if you need more flexibility or want to use alternate features vectors, etc.

Dimensionality reduction

Once the features has been extracted, they are projected into a lower dimensional subspace using Principal Component Analysis (PCA):

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

Note

Equivalently, this can also be achieved by running the following individual commands:

$ ./bin/pca_train.py --features-dir features/lbph --pca-dir features --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/linear_project.py --features-dir features/lbph --algorithm-dir features --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

PLDA modeling and scoring

PLDA is then applied on the dimensionality reduced features.

This involves three different steps:
  1. Training
  2. Model enrollment
  3. Scoring

The following command will perform all these steps:

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

Note

Equivalently, this can also be achieved by running the following individual commands:

$ ./bin/plda_train.py --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/plda_enroll.py --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/plda_scores.py --group dev --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/plda_scores.py --group eval --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

Then, the HTER on the evaluation set can be obtained using the evaluation script from the bob library as follows:

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

The HTER on the evaluation set, when using the the EER on the development set as the criterium for the threshold, corresponds to the PLDA value reported on Table 3 of the article mentioned above.

If you want to reproduce the Figure 2 of the article, you can run the following commands (instead of the previous one):

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

Then, the value of the HTER on Table 3 corresponds to the one, where the full training set is used, and might similarly be obtained as follows:

$ ./bin/bob_compute_perf.py -d /PATH/TO/MULTIPIE/OUTPUT_DIR/U/plda_subworld_76/scores/scores-dev -t /PATH/TO/MULTIPIE/OUTPUT_DIR/U/plda_subworld_76/scores/scores-eval -x

Note

Equivalently, this can also be achieved by running the following individual commands. Be aware that the commands within the loop are independent and monothreaded. Furthermore, you could break the loop and call several of these commands at the same time if your CPU has several cores:

$ for k in 2 4 6 8 10 14 19 29 38 48 57 67 76; do \
    ./bin/plda.py --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/ --world-nshots $k --plda-dir plda_subworld_${k}; \
  done
$ ./bin/plot_figure2.py --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

The previous commands will run the PLDA toolchain several times for a varying number of training samples. Please note, that this will require a lot of time to complete (one to two days on a recent workstation such as one with an Intel Core i7 CPU).

Note

If you compare your obtained figure with the Figure 2 of the published article, you will observe slight differences. This is caused by two different aspects:

  1. The features for the paper were generated using a version of Bob that is unofficial (which means older than the first official release), whereas the features currently generated rely on Bob 1.2.0. Many improvements were performed in the implementations of the preprocessing techniques (Face cropping and Tan Triggs algorithm) as well as in the LBP implementation.
  2. The order of the files obtained (and now sorted) from the database API. For instance, when applying PCA, the input matrix will be different depending on the order of the file used to build this matrix.

Baseline: LBP histogram with Chi square scoring

The LBP histogram features might be used in combination with a distance such as the Chi Square distance, to obtain a face recognition system.

This involves two different steps:
  1. Model enrollment
  2. Scoring (with a chi square distance)

The following command will perform all these steps:

$ ./bin/toolchain_lbph.py --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

Note

Equivalently, this can also be achieved by running the following individual commands:

$ ./bin/meanmodel_enroll.py --features-dir features/lbph --algorithm-dir lbph_chisquare --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir features/lbph --algorithm-dir lbph_chisquare --distance chi_square --group dev --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir features/lbph --algorithm-dir lbph_chisquare --distance chi_square --group eval --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

Then, the HTER on the evaluation set can be obtained using the evaluation script from the bob library as follows:

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

This value corresponds to the LBP histogram (chi square) value reported on Table 3 of the PLDA article (Once more, be aware of slight differences due to the implementation changes on the feature extraction process).

Baseline: LDA on the PCA projected LBP histograms

The PCA projected LBP histogram features were also used in combination with the Fisher's LDA classification technique (commonly called Fisherfaces in the face recognition litterature).

This involves three different steps:
  1. LDA subspace training
  2. Model enrollment
  3. Scoring (with an Euclidean distance)

The following command will perform all these steps:

$ ./bin/toolchain_lda.py --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

Note

Equivalently, this can also be achieved by running the following individual commands:

$ ./bin/lda_train.py --lda-dir lda --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/linear_project.py --lda-dir lda --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/meanmodel_enroll.py --features-dir lda/lbph_projected --algorithm-dir lda --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir lda/lbph_projected --algorithm-dir lda --distance euclidean --group dev --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir lda/lbph_projected --algorithm-dir lda --distance euclidean --group eval --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

Then, the HTER on the evaluation set can be obtained using the evaluation script from the bob library as follows:

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

This value corresponds to the LDA baseline reported on Table 3 of the PLDA article (Once more, be aware of slight differences due to the implementation changes in the feature extraction process). These results are obtained for a LDA subspace of rank 64, which was found as an optimal LDA subspace size, when we tuned this parameter using the initial features of the paper.

Reporting bugs

The package is open source and maintained via github.

If you are facing technical issues to be able to run the scripts of this package, please send a message on the bob's mailing list.

If you find a problem wrt. to this satelitte package, you can file a ticket on the github issue tracker of this satellite package.

If you find a problem wrt. to the PLDA implementation, you can file an ticket on Bob's issue tracker .

Please follow these guidelines when (or even better before) reporting any bug.

 
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