A Gabor feature classifier for face recognition

Abstract

This paper describes a novel Gabor feature classifier (GFC) method for face recognition. The GFC method employs an enhanced Fisher discrimination model on an augmented Gabor feature vector, which is derived from the Gabor wavelet transformation of face images. The Gabor wavelets, whose kernels are similar to the 2D receptive field profiles of the mammalian cortical simple cells, exhibit desirable characteristics of spatial locality and orientation selectivity. As a result, the Gabor transformed face images produce salient local and discriminating features that are suitable for face recognition. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images, which involve different illumination and varied facial expressions of 200 subjects. The effectiveness of the novel GFC method is shown in terms of both absolute performance indices and comparative performance against some popular face recognition schemes such as the eigenfaces method and some other Gabor wavelet based classification methods. In particular, the novel GFC method achieves 100% recognition accuracy using only 62 features.