Human face detection using angular radial transform and support vector machines

This paper presents a new face detection method. For a potential face pattern, a histogram equalized intensity map and a local intensity variance map are created to normalize the pattern photometrically. We then view these two maps as geometric shapes and apply the angular radial transform (ART) to derive a compact representation of the pattern. The ART transform coefficients are then used as input to a support vector machine (SVM) to determine the presence or absence of a face in the pattern. We also develop a SVM based skin color detection technique as a preprocessing step and only search image regions that contain sufficient large number of skin pixels thus greatly enhancing the detection speed. Experimental results are presented to demonstrate the effectiveness of the new method.