Author: Nesli Erdogmus


 Within- and Cross- Database Evaluations for Gender Classification via BeFIT Protocols

This package implements the grid-search for optimal LBP-based algorithm parameters for gender classification on BANCA and MOBIO databases and the generic algorithm that is used to test the final system on FERET, MORPH-II and LFW databases using the BeFIT protocols. It is possible to conduct both within- and cross- database gender classification experiments. The details can be found in the paper `Within- and Cross- Database Evaluations for Gender Classification via BeFIT Protocols`, by N. Erdogmus, M. Vanoni and S. Marcel.

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

1. The original paper with the gender classification algorithms explained in details::

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 databases used in the paper is publicly available and should be downloaded and installed **prior** to try using the programs described in this package (except FERET for which the utilized subset is included in the package).

LFW:  <>


.. 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 estimate.gender

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

  $ easy_install estimate.gender

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