Code covered by the BSD License

Objects/Faces Detection Toolbox

Sebastien PARIS (view profile)

12 May 2009 (Updated )

Objects/Faces detection using Local Binary Patterns and Haar features

auroc(tp, fp)
```function A = auroc(tp, fp)
%
% AUROC - area under ROC curve
%
%    An ROC (receiver operator characteristic) curve is a plot of the true
%    positive rate as a function of the false positive rate of a classifier
%    system.  The area under the ROC curve is a reasonable performance
%    statistic for classifier systems assuming no knowledge of the true ratio
%    of misclassification costs.
%
%    A = AUROC(TP, FP) computes the area under the ROC curve, where TP and FP
%    are column vectors defining the ROC or ROCCH curve of a classifier
%    system.
%
%    [1] Fawcett, T., "ROC graphs : Notes and practical
%        considerations for researchers", Technical report, HP
%        Laboratories, MS 1143, 1501 Page Mill Road, Palo Alto
%        CA 94304, USA, April 2004.
%
%    See also : ROC, ROCCH

%
% File        : auroc.m
%
% Date        : Wednesdaay 11th November 2004
%
% Author      : Dr Gavin C. Cawley
%
% Description : Calculate the area under the ROC curve for a two-class
%               probabilistic classifier.
%
% References  : [1] Fawcett, T., "ROC graphs : Notes and practical
%                   considerations for researchers", Technical report, HP
%                   Laboratories, MS 1143, 1501 Page Mill Road, Palo Alto
%                   CA 94304, USA, April 2004.
%
% History     : 22/03/2001 - v1.00
%               10/11/2004 - v1.01 minor improvements to comments etc.
%

n = size(tp, 1);
A = sum((fp(2:n) - fp(1:n-1)).*(tp(2:n)+tp(1:n-1)))/2;

% bye bye...

```

Contact us