Author: Tiago de Freitas Pereira


LBP-TOP based countermeasure against facial spoofing attacks

This package implements an LBP-TOP based countermeasure to spoofing attacks to face recognition systems as described at the paper LBP-TOP based countermeasure against facial spoofing attacks, International Workshop on Computer Vision With Local Binary Pattern Variants, 2012.

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::

      author = {Pereira, Tiago de Freitas and Anjos, Andr{\'{e}} and De Martino, Jos{\'{e}} Mario and Marcel, S{\'{e}}bastien},
      keywords = {Attack, Countermeasures, Counter-Spoofing, Face Recognition, Liveness Detection, Replay, Spoofing},
      month = nov,
      year = {2012},
      title = {LBP-TOP based countermeasure against facial spoofing attacks},
      journal = {International Workshop on Computer Vision With Local Binary Pattern Variants - ACCV},

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},

Raw Data
The dataset used in the paper is REPLAY-ATTACK database and it is publicly available. It should be downloaded and
installed **prior** to using the programs described in this package. Visit
`the REPLAY-ATTACK database page <>`_ for more information.


.. 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.lbptop

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

  $ easy_install antispoofing.lbptop

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-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 th