Face Regularization Preprocessing And Evolvable Face Recognition Based On Wavelet Transform

Face Recognition Research comes of the nice concept (humanization of computers of thousands of scientific workers. It aims at endowing computers with the ability to identify different human beings according to his/her face image. The research has both significant theoretic values and wide market applications. Face Recognition problem is a typical image processing, pattern analysis, understanding and classificati on problem, closely related to many disciplines such as Pattern Recognition, Natural and Evolutionary Computation, Computer Vision, Intelligent Human-Computer Interaction, Computer Graphics, and Cognitive Psychology etc. As one of key techniques of biological information recognition, Face Recognition has significant application in several areas of national security, public security, information security, finance etc. After more than 30 years' development, Face Recognition has made great progress. The state-of-the-art Face Recognition system can perform identification successfully under well-controlled environment. However, evaluation results and practictical experience have shown that Face Recognition technologies are currently far from mature. The following key issue is especially pivotal: (1) The accurate facial feature location problem, which is the prerequisite for sequent feature exaction and Classification; (2) Face Preprocessing and efficient face representation and recognition methods.In this thesis, the second of the above-mentioned key issues are studied.The main contribution of this thesis includes:1. The thesis summarizes existing main face image database and discuss the important problem to be confronted with in Face Recognition area: Face characteristic Description.2. The thesis derives the whole expression comparatively and takes the lead in using a Regularization method based on wavelet transform as an important degraded face restoring method in face preprocessing. The results show better effect than common Regularization method to some extent.3. The thesis discusses the Gabor Wavelet Networks and deal with it by evolutionary optimization and put forward and implement new face recognition methods: IOEA-GWN. Experiments shows higher recognition rates under various changed factors.4. The thesis gives out analysis and prospect of further studies and draw the conclusion: GWN can be optimized by several evolutionary algorithms to improve the effect of face recognition rates and these methods can be used by researchers from other pattern recognition area.