bob.paper.CVPRW_2016 1.0.2

Running the experiments as given in paper: "Heterogeneous Face Recognition using Inter-Session Variability Modelling".

Heterogeneous Face Recognition using Inter-Session Variability Modelling

This package provides the source code to run the experiments published in the paper Heterogeneous Face Recognition using Inter-Session Variability Modelling.

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

1. The original paper with the counter-measure explained in details:

   @inproceedings{Pereira_CVPRW2016,
     author = {Pereira, Tiago de Freitas and Marcel, S{\'{e}}bastien},
     keywords = {Face Recognition, Session Variability Modelling, Heterogeneous Face Recognition},
     month = jun,
     year = {2016},
     title = {Heterogeneous Face Recognition using Inter-Session Variability Modelling},
     journal = {IEEE Computer Society Workshop on Biometrics - CVPRW 2016},
   }

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},
   }

Raw Data

This package does not provide the dataset used in the paper. They must be downloaded separately from CUHK_CUFS (http://mmlab.ie.cuhk.edu.hk/archive/facesketch.html) and CBSR NIR-VIS-2.0 (http://www.cbsr.ia.ac.cn/english/NIR-VIS-2.0-Database.html).

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/antispoofing.lbptop to download the latest stable version of this package.

There are 2 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 bob.paper.CVPRW_2016

You can also do the same with easy_install:

$ easy_install bob.paper.CVPRW_2016

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. 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

Reproducibility

Please, check our documentation in order to reproduce the results of the paper.