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Scientific/Engineering Python 3 Console plda probalistic linear discriminant analysis machine learning face recognition bob xbob

Author

Laurent El Shafey

License

GPLv3

Dependencies

This package contains scripts that show 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.

Contents

References

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

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}
}

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},
}

If you decide to use the Multi-PIE database, you should 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},
}

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}
}

If you decide to use the Labeled Faces in the Wild (LFW) database, you should mention the following paper, where it is introduced:

@TechReport{LFWTech,
  author = {Gary B. Huang and Manu Ramesh and Tamara Berg and Erik Learned-Miller},
  title = {Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments},
  institution = {University of Massachusetts, Amherst},
  year = {2007},
  number = {07-49},
  month =  oct,
}

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. In both cases, you must first install Bob (>= 1.2.0), whose installation process is described in the user guide.

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.tpami2013-1.0.0.zip
$ unzip xbob.paper.tpami2013-1.0.0.zip
$ cd xbob.paper.tpami2013-1.0.0

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.

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 Probabilistic Linear Discriminant Analysis (PLDA) modeling on the Fisher's iris dataset. It performs the following tasks:

Train a PLDA model using the first two classes of the dataset

Enroll a class-specific PLDA model for the third class of the dataset

Compute (verification) scores for both positive and negative samples

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 all the experiments of the article on both Labeled Faces in the Wild and Multi-PIE using the PLDA algorithm. In particular, the value of the accuracy reported in Table 2, the Figure 2 and the HTER reported on Table 3 can be easily reproduced, by following the steps described below.

Be aware that all the scripts provide several optional arguments that are very useful if you wish to use your own features or your own parameters.

Keep in mind that the results published in the paper were obtained with a pre-release of Bob (older than 1.0.0). You might hence observe slight differences when running the scripts with Bob 1.2.0.

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 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.

Labeled Faces in the Wild dataset

The experiments of this section are performed on the LFW (Labeled Faces in the Wild) protocol. The features are publicly available and will be automatically downloaded from this webpage if you follow the instruction below. They were extracted on the LFW images aligned with the funneling algorithm.

Getting the features and converting them into HDF5 format

The following command will download a tarball with the SIFT features, extract the content of the archive and convert the features into a suitable HDF5 format for Bob:

$ ./bin/lfw_features.py --output-dir /PATH/TO/LFW/DATABASE/

PCA+PLDA toolchain on LFW

Once the features have been extracted, the dimensionality is reduced using Principal Component Analysis (PCA), before applying PLDA modeling. These steps are combined in the following script, that will run the PCA+PLDA toolchain on the specified protocol:

$ ./bin/toolchain_pcaplda.py --features-dir /PATH/TO/LFW/DATABASE/lfw_funneled --protocol view1 --output-dir /PATH/TO/LFW/OUTPUT_DIR/

To report the final performance on LFW, it is required to run 10 experiments on view 2 in a leave-one-out cross validation scheme. We provide the following script for this purpose:

$ ./bin/experiment_pcaplda_lfw.py -features-dir /PATH/TO/LFW/DATABASE/lfw_funneled --output-dir /PATH/TO/LFW/OUTPUT_DIR/

Note

The previous script is monothreaded and will run the 10 independent view 2 experiments in a sequence. If you have a multi-core CPU, you could split this script into several shorter jobs, by splitting the loop below, which will at the end be equivalent to the previous command:

$ for k in `seq 1 10`; do \
    ./bin/toolchain_pcaplda.py --features-dir /PATH/TO/LFW/DATABASE/lfw_funneled --protocol view2-fold${k} --output-dir /PATH/TO/LFW/OUTPUT_DIR/ ; \
  done

Summarizing the results as in Table 2

Once the previous experiments have successfully completed, you can use the following script to plot Table 2, that will estimate the mean accuracy as well as the standard error of the mean on the 10 experiments of LFW view2:

$ ./bin/plot_table2.py --output-dir /PATH/TO/LFW/OUTPUT_DIR/

Note

Compared to the results published in the article, there are slight differences caused by both the order of the training files when applying PCA, and the lists used to split the LFW training set into a training set and a validation set (The validation set is use to select the verification threshold to apply on the test set).

Multi-PIE dataset

The experiments of this section are performed on the U protocol of the Multi-PIE dataset. The filelists associated with this protocol can be found on this website.

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 Local Binary Patters (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 /PATH/TO/MULTIPIE/OUTPUT_DIR/U/features/lbph --pca-dir features --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/linear_project.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/features/lbph --algorithm-dir features --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

Proposed system: PLDA modeling and scoring

PLDA is then applied on the dimensionality reduced features.

This involves three different steps:

Training
Model enrollment
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 EER on the development set as the criterion 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/

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/toolchain_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 (a bit less than two days on a recent workstation such as one with an Intel Core i7 CPU).

Then, the value of the HTER on Table 3 of the article (for the PLDA system) 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

If you compare your obtained figure with the Figure 2 of the published article, you will observe slight differences. This does not affect at all the global trends and conclusions shown in the article. This is caused by two different aspects:

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.
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 1: PCA on the LBP histograms

The LBP histogram features were used in combination with the PCA classification technique (commonly called Eigenfaces in the face recognition literature).

This involves three different steps:

PCA subspace training
Model enrollment
Scoring (with an Euclidean distance)

The following command will perform all these steps:

$ ./bin/toolchain_pca.py --n-outputs 2048 --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 /PATH/TO/MULTIPIE/OUTPUT_DIR/U/features/lbph --n-outputs 2048 --pca-dir pca_euclidean --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/linear_project.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/features/lbph --algorithm-dir pca_euclidean --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/meanmodel_enroll.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/pca_euclidean/lbph_projected --algorithm-dir pca_euclidean --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/pca_euclidean/lbph_projected --algorithm-dir pca_euclidean --distance euclidean --group dev --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/pca_euclidean/lbph_projected --algorithm-dir pca_euclidean --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/pca_euclidean/scores/scores-dev -t /PATH/TO/MULTIPIE/OUTPUT_DIR/U/pca_euclidean/scores/scores-eval -x

This value corresponds to the one of the PCA baseline reported on Table 3 of the article (Once more, be aware of differences due to the implementation changes in the feature extraction process and algorithm parameters that have not been kept). These results are obtained for a PCA subspace of rank 2048, which was found to be the optimal PCA subspace size, when we tuned this parameter using the LBPH features.

Note

In contrast to what one sentence of the article suggests, we did not apply the PCA baseline on the dimensionality-reduced PCA features. This would mean to apply consecutively twice, the same PCA dimensionality reduction technique, which does not make much sense. In contrast, we apply this PCA technique to the LBPH features, tuning the PCA subspace size.

Baseline 2: LDA on the PCA projected LBP histograms

The PCA projected LBP histogram features considered for the PLDA system were also used in combination with the Fisher's Linear Discriminant Analysis (LDA) classification technique (commonly called Fisherfaces in the face recognition literature).

This involves three different steps:

LDA subspace training
Model enrollment
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 --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/features/lbph_projected --lda-dir lda_euclidean --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/linear_project.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/features/lbph_projected --algorithm-dir lda_euclidean --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/meanmodel_enroll.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/lda_euclidean/lbph_projected --algorithm-dir lda_euclidean --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/lda_euclidean/lbph_projected --algorithm-dir lda_euclidean --distance euclidean --group dev --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/lda_euclidean/lbph_projected --algorithm-dir lda_euclidean --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_euclidean/scores/scores-dev -t /PATH/TO/MULTIPIE/OUTPUT_DIR/U/lda_euclidean/scores/scores-eval -x

This value corresponds to the one of 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 to be the optimal LDA subspace size, when we tuned this parameter using the initial LBPH features.

Baseline 3: LBP histogram classification 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:

Model enrollment
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 /PATH/TO/MULTIPIE/OUTPUT_DIR/U/features/lbph --algorithm-dir lbph_chisquare --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/features/lbph --algorithm-dir lbph_chisquare --distance chi_square --group dev --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/
$ ./bin/distance_scores.py --features-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/U/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 one of the LBP histogram (chi square) baseline reported on Table 3 of article (Once more, be aware of slight differences due to the implementation changes on the feature extraction process).

Summarizing the results as in Table 3

If you successfully run all the previous experiments, you could get a summary of the performances, as in Table 3, by running the following command:

$ ./bin/plot_table3.py --output-dir /PATH/TO/MULTIPIE/OUTPUT_DIR/

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. this satelitte package, you can file a ticket on the github issue tracker of this satellite package.

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

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

PyPM Index

xbob.paper.tpami2013 1.0.0

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

Links

Author

License

Dependencies

References

Installation

Using an automatic installer

Using `zc.buildout`

PLDA tutorial

Reproducing experiments

Note for Grid Users

Labeled Faces in the Wild dataset

Getting the features and converting them into HDF5 format

PCA+PLDA toolchain on LFW

Summarizing the results as in Table 2

Multi-PIE dataset

Getting the data

Feature extraction

Dimensionality reduction

Proposed system: PLDA modeling and scoring

Baseline 1: PCA on the LBP histograms

Baseline 2: LDA on the PCA projected LBP histograms

Baseline 3: LBP histogram classification with Chi square scoring

Summarizing the results as in Table 3

Reporting bugs

Subscribe to package updates

What does the lock icon mean?

Need custom builds or support?

Plan on re-distributing ActivePython?

Accounts

PyPM

Feedback & Information

ActiveState

PyPM Index

xbob.paper.tpami2013 1.0.0 Example on how to use the scalable implementation of PLDA and how to reproduce experiments of the article

Links

Author

License

Dependencies

Subscribe to package updates

What does the lock icon mean?

Need custom builds or support?

Plan on re-distributing ActivePython?

Accounts

PyPM

Feedback & Information

ActiveState

xbob.paper.tpami2013 1.0.0

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