Combining Multiple Features for High Performance Face Recognition System

Abstract

This paper proposes the combination multiple facial feature extraction methods and probabilistic neural network for facial recognition. Firstly, we use horizontal projection of 2-D image to obtain accumulated energy profile signal. Secondly, we obtain the statistical distribution of facial gray images. Finally, we adopt wavelet transform to extract low frequency coefficients from 1-D energy profile signal and statistical distribution of face gray level values as feature vectors, which is applied with probabilistic neural network in facial identification and facial matching. Thus, the proposed method is evaluated on the ORL face database for face recognition. Besides, the face recognition system is also built on PC, and it is evaluated on real data set by the proposed algorithm. The experiment results show that the proposed method possesses the excellent performance. Because of low complexity, it is also suitable for a hardware-friendly and resource-constrained embedded environment.