Analysis of training parameters for classifiers based on Haar-like features to detect human faces

This paper analyzes the performance of the Haar-like feature based classifier for detection of face with fewer features. The lower dimensional feature space representation of the image may reduce the computational burden compromising the accuracy in detection of faces with varying orientations. In this work we train the classifier with positive instances of different orientations under such feature constraint. The training parameters like maximum deviation and maximum angle are varied to form different classifiers. Experimental results show optimum values of the design parameters can produce good performance of the classifier to detect frontal as well as tilted human faces.