=================================================== The FAR-FRR confidence interval of the DET curve for Bob

This example demonstrates how to extend Bob by providing a new performance measurement for measuring the confidence interval of the DET curve.

Originally described in the paper: Poh, N.; Martin, A.; Bengio, S., "Performance Generalization in Biometric Authentication Using Joint User-Specific and Sample Bootstraps," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.29, no.3, pp.492,498, March 2007

Installation

First, you have to install bob <http://www.idiap.ch/software/bob> following the instructions there <http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/Installation.html>.

.. 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/xbob.measure.Bootstraps <http://pypi.python.org/pypi/xbob.>_ to download the latest stable version of this package.

There are two 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. In both cases, the two dependences listed above will be automatically downloaded and installed.

Using an automatic installer

Using pip is the easiest (shell commands are marked with a $ signal)::

$ pip install xbob.measure.Bootstraps

You can also do the same with easy_install::

$ easy_install xbob.measure.Bootstraps

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/xbob.measure.Bootstraps> 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

These two 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 this package. Because this package makes use of Bob_, you must make sure that the bootstrap.py script is called with the same interpreter used to build Bob, or unexpected problems might occur.

If Bob is installed by the administrator of your system, it is safe to consider it uses the default python interpreter. In this case, the above 3 command lines should work as expected. If you have Bob installed somewhere else on a private directory, edit the file buildout.cfg before running ./bin/buildout. Find the section named buildout and edit or add the line prefixes to point to the directory where Bob is installed or built. For example::

[buildout]
...
prefixes=/Users/crazyfox/work/bob/build

User Guide

It is assumed you have followed the installation instructions for the package and got this package installed.

Below, we provide an example of how to appy jointbootstraps to plot the confidence curve, from the python universe::

import Bootstraps

predefine a list of confidence rates

confidence=[.25, 0.5, .75] n_user_bstrp=30 n_sample_bstrp=3

Read The four column file needs to be in the same format as described in the five_column function,

and the "test label" (column 4) has to contain the test/probe file name. please refer the functions of bob.measure.load.cmc_four_column, bob.measure.load.cmc_five_column to load or generate the "cmc scores". Bootstraps.JointBootstraps_plot(cmc_scores,confidence,n_user_bstrp,n_sample_bstrp=3)

Plot the DET curve

pos_scores=[] neg_scores=[] for Id in range(0,len(cmc_scores)): pos_scores=numpy.append(pos_scores, cmc_scores[Id][1]) neg_scores=numpy.append(neg_scores, cmc_scores[Id][0]) bob.measure.plot.det(neg_scores,pos_scores,100) bob.measure.plot.det_axis([.05, 60, .05, 60]) pyplot.xlabel("False Acceptance Rate") pyplot.ylabel("False Rejection Rate") pyplot.title("The FAR-FRR confidence interval of the DET curve") pyplot.grid() pyplot.savefig("Confidence_DET.png") pyplot.close()