Design And Implementation Of Face Recognition Algorithm Based On2DPCA+PCA Of Gabor Wavelet

Recent years, face recognition technology has achieved enormous progress, but its recognition precision in practical applications still cannot satisfy the expectant demands of people, especially under the condition that variations of illumination, photographing azimuth or other disturbance exist in the image. To resolve these problems, this paper researched the existing algorithm of face recognition based on the PCA (principal component analysis),2DPCA (2dimensional principal component analysis), Dia PCA (Diagonal principal component analysis), Dia2DPCA(Diagonal2dimensional principal component analysis) etc, compared the face recognition of their recognition rate and put forward a new method of face recognition-the face recognition using2DPCA+PCA of Gabor filter responses. The main work and contributions of the dissertation are as follows:(1) The framework of structure and procedures of face recognition system is comprehensive overviewed. Mainly introduces several main methods before the face recognition of image processing and in the process of recognition. Then corresponding to the most core part of the whole face recognition system, that is feature extraction and selection, introducing four face recognition methods according to the order from simple to complex, from the original to advanced(2) Two-dimensional recognition applications are wavelets transform and its response characteristics in researched. Two-dimensional wavelets transform is realized by computing the convolutions of a bank of two-dimensional Gabor filters and the grey values of pixels in an area around a given position in an image. Two-dimensional Gabor wavelets seem to be a good approximation to the receptive fields of the simple cells in the visual cortex of mammalians. In this dissertation, it is validated by the computational results that the local features of face images can be represented through selecting the parameters of Gabor filters, and this kind of representation has the merit of insensitiveness to the absolute brightness of the capturing environment. Recognition based on two-dimensional Gabor wavelets transform surpasses the one based on the grey values of the original image directly. As carried on the comprehensive experiment to each direction and the frequency Gabor person face characteristic, draws the conclusion:Is all essential regarding the mufti-posture person face recognition each direction characteristic information, may select the partial frequencies the Gabor filter to attribute the human face.(3) For feature extraction, this paper first implements two typical methods:PC A and2DPCA, analyzes and experiments with them. And then the diagonal principal component analysis (DiaPCA) method and diagonal2D method of principal component analysis (Dia2DPCA) are research and proof, What is more, I present a new method; the2DPCA+PCA feature extraction.The highest recognition rate of the method this paper proposed can reach96%. When the training samples are not enough, the recognition rate of Gabor+2DPCA+PCA is significantly higher than the direct application of2DPCA and PCA.