Face Detection Based on Multiple Regression and Recognition Support Vector Machines

This paper presents a novel approach to face detection. A potential face pattern is first filtered by a Gaussian derivative filter bank to generate a set of derivative images, which are then transformed by the Angular Radial Transform (ART) to form a compact set of representation feature vectors. Using these feature vectors for face detection is based on a two level multiple support vector machines (SVMs) strategy. At the first level, a separate SVM is trained for each derivative image to indicate the presence/absence of a face in the input based on the features from that derivative image alone. These SVMs are trained as binary classifiers but used for regression in the sense that they output continuous values. At the second level, a single SVM takes the outputs of the first level SVMs as input to make the overall and final decision to determine whether the current input is a face or nonface pattern. Experimental results are presented to demonstrate the effectiveness of the method.