Metadata-Version: 1.1
Name: bob.paper.CVPRW_2016
Version: 1.0.2
Summary: Running the experiments as given in paper: "Heterogeneous Face Recognition using Inter-Session Variability Modelling".
Home-page: http://gitlab.idiap.ch/
Author: Tiago de Freitas Pereira
Author-email: tiago.pereira@idiap.ch
License: BSD
Description: .. image:: https://gitlab.idiap.ch/biometric/bob.paper.CVPRW_2016/badges/master/build.svg?
           :target: https://gitlab.idiap.ch/biometric/bob.paper.CVPRW_2016/commits/master
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           :target: https://pypi.python.org/pypi/bob.paper.CVPRW_2016
        .. image:: https://img.shields.io/badge/original-data--files-a000a0.png
           :target: http://www.cbsr.ia.ac.cn/english/NIR-VIS-2.0-Database.html
        .. image:: https://img.shields.io/badge/original-data--files-a000a0.png
           :target: http://mmlab.ie.cuhk.edu.hk/archive/facesketch.html
        
        ========================================================================
        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 <http://publications.idiap.ch/index.php/publications/show/3370>`_.
        
        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
          <http://pypi.python.org/pypi/bob.paper.CVPRW_2016>`_ 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
        <http://pypi.python.org/pypi/pip/>`_ (or `easy_install
        <http://pypi.python.org/pypi/setuptools>`_) or manually download, unpack and
        use `zc.buildout <http://pypi.python.org/pypi/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
        <http://pypi.python.org/pypi/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
        <http://pypi.python.org/pypi/bob.paper.CVPRW_2016>`_ and unpack it in your
        working area. The installation of the toolkit itself uses `buildout
        <http://www.buildout.org/>`_. 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.
        
          
          
        .. _Bob: http://idiap.github.io/bob/  
        .. _virtualbox: http://www.virtualbox.org
        .. _bob_bio: https://pypi.python.org/pypi/bob.bio.gmm/
        
Keywords: bob
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
