| With the development of science and technology,the progress of human society, the traditional identification is easy to lose,easy to be cracked and it has not play an identifiable role. People need a more secure and reliable identification technology. Biometric is unique,easy to lose and replication characteristics of good meet the needs of the identification. With the development of computer science and technology and biomedical makes use of biometric identification has become possible. In the field of biometric identification,face recognition with the advantages of operation is fast and simple,the results are intuitive,accurate and reliable,do not need co-ordination,has become the focus of attention.The principal component analysis (PC A) to extract high dimensional face image of the main element,making the images are processed in low-dimensional space and it reduces the difficulty of image processing. PCA solves effectively the problem of high dimension image space and it has become a very important theory in face recognition field. This paper is in this context of writing from.In order to quickly and efficiently extract facial features, this paper presents an improved PCA algorithm, combined with gamma transform and wavelet transform face recognition algorithm for image processing.This algorithm first complete picture of the face of gamma changes, reduced interference light and a series of non-linear factors;Next, the complete change of face image wavelet decomposition, with face pictures to represent the low-frequency component to obtain initial face image;Finally, the low-frequency component of the face acquired complete PCA facial feature extraction, classification using support vector machines to classify and recognize, to get the final recognition result.Complete testing on ORL face database, and complete the FPGA implementation algorithms, the recognition rate of this method is more general PCA method improved 4.5%. |
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