Author: Ivana Chingovska


 Counter-Measures to Face Spoofing Attacks using Local Binary Patterns (LBP)

This package implements the LBP counter-measure to spoofing attacks to face
recognition systems as described at the paper `On the Effectiveness of Local
Binary Patterns in Face Anti-spoofing`, by Chingovska, Anjos and Marcel,
presented at the IEEE BioSIG 2012 meeting.

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

1. The `original paper <>`_ with the counter-measure explained in details::

    author = {Chingovska, Ivana and Anjos, Andr{\'{e}} and Marcel, S{\'{e}}bastien},
    keywords = {Attack, Counter-Measures, Counter-Spoofing, Face Recognition, Liveness Detection, Replay, Spoofing},
    month = sep,
    title = {On the Effectiveness of Local Binary Patterns in Face Anti-spoofing},
    journal = {IEEE BIOSIG 2012},
    year = {2012},
2. `Bob <>`_ as the core framework used to run the experiments::

        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},

If you wish to report problems or improvements concerning this code, please
contact the authors of the above mentioned papers.

Raw data

The data used in the paper is publicly available and should be downloaded and
installed **prior** to try using the programs described in this package. Visit
`the REPLAY-ATTACK database portal
<>`_ for more information.

This satellite package can also work with the `CASIA_FASD database <>`_. 


.. 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 `
  <>`_ 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 antispoofing.lbp

You can also do the same with ``easy_install``::

  $ easy_install antispoofing.lbp

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 
  $ ./bin/buildout

These 2 commands should download and install all non-install