Face recognition method based on independent component analysis and BP neural network

Abstract

In this paper a new face recognition method combining independent component analysis (ICA) and BP neural network, named ICABP method, is proposed. Researchers have shown that ICA using higher order statistics is more powerful for face recognition than PCA using up to second order statistics only. However, when the database includes faces with various expressions and different orientations, the superiority of ICA method cannot be shown obviously. In this paper, the FastICA algorithm is used to extract the independent sources from the face images. Then the conventional minimum Euclidean distance method is replaced by an improved BP neural network with one hidden layer to recognize the faces. The function of local features extraction of ICA and the adaptability of BP neural network are combined perfectly. The experimental results show that our ICABP method is an effective and feasible face recognition method.