Detection and Identification Rate
Project description
This example demonstrates how to extend Bob by providing a new performance measurement for measuring the open-set identification
Installation
First, you have to install bob following the instructions there.
There are two 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. 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.idmeasure
You can also do the same with easy_install:
$ easy_install xbob.measure.idmeasure
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 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.
User Guide
It is assumed you have followed the installation instructions for the package and got this package installed.
Two functions, DIR and DIR_plot, are in this package to compute and plot the detection and identification rates by given the predefined false acceptance rates. The descriptions of these function are presented below:
DIR(cmc_scores, far_list): ========================== Calculates the Detection and Identification Rate from the give input and a vector of specified false acceptance rates Keyword attributes: cmc_scores List of two-element tuples. Each of the tuples contains the negative and the positive scores for one test item. far_list Array of predefined false acceptance rates. Return: List of two-element tuples, namely detection and identificatio rate. Each of the tuples contains the probability that the rank r of the positive score and the corresponding false acceptance rate. r is computed as the number of negative scores that are higher than the positive score. DIR_plot(cmc_scores, far_list, logx = True, **kwargs): ====================================================== Plot the Detection and Identification Rate from the give input and a vector of specified false acceptance rates Keyword attributes: cmc_scores List of two-element tuples. Each of the tuples contains the negative and the positive scores for one test item. far_list Array of predefined false acceptance rates. logx Boolean input, if it is true, the x-axis is in log scale. kwargs A dictionary of extra plotting parameters, that is passed directly to matplotlib.pyplot.plot Note: This function does not initiate and save the figure instance, it only issues the plotting commands. Every user is responsible for setting up and saving the figure as it best fits his purpose.
Below, we provide an example of how to appy DIR_plot to plot the DIR curve, from the python universe:
>>> import idmeasure # predefine a list of false acceptance rates >>> FAR=[.01, 0.1, 1] #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". >>> idmeasure.DIR_plot(cmc_scores, FAR) >>>pyplot.xlabel("Rank") >>>pyplot.ylabel("Identification Rate (%)") >>>pyplot.title("Detection and Identification Rate Identification Experiment") >>>pyplot.grid() >>>pyplot.savefig("eigenfaceDIR.png") >>>pyplot.close()
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