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I am reading paper by Viola and Jones. There they have used ROC curve to measure the accuracy of their classifier.

https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf

Could someone please explain how the ROC curve is plotted in case of binary classifier like face or non face? I mean how is the data points obtained.

(X,Y)= (falsepositive, correctdetection rate)

Do I have to calculate these points for every positives and negatives of my training data set. But my positive and negative data sets are of different sizes. I am bit confused.

Nemi Bhattarai
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1 Answers1

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ROC curve - Receiver operating characteristic is a measure of the accuracy of their classifier. As much as the area under the curve is larger the classifier is more accurate. In order to increase the area under the curve, the classifier needs to have a high value on the y-axis. That means to have a good TPR = true positive rate.

To calculate the ROC you first need to plot a graph of No' of instance as a function of the result of the AdaBoost classifier. After doing that in order to plot the graph you need to move the threshold of the AdaBoost classifier and calculate the TPR and FPR of each point.

Chananel Zaguri
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