Face recognition by distribution specific feature extraction

A new pattern recognition approach to face recognition is presented that can deal with drastic differences in the appearance of a face. Given a pair of sample sets of facial images with potential correspondences, each being drawn from a distinctive distribution, the algorithm reliability finds correspondences over those different distributions. Unlike the traditional approaches that model the face images as having a consistent distribution and so use the same feature extraction function for both of the image sets, the new method applies to each sample a function specific to the distribution from which it is drawn. This function is derived by maximizing a newly defined class-separability criterion over the different distributions. Results of face recognition are presented on images including drivers´ license pictures. Drastic improvements are shown over algorithms based on the traditional Fisher´s discriminant analysis