Face Recognition by Distribution Specific Feature Extraction

Abstract

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.