Research On Feature Extraction And Selection Algorithms Of Face Recognition

Face recognition technology has been the active field of computer vision, image processing and patern recogniton. After serval decades rapid development, it has been widely applied to surveillance and security, human-computer intelligent interaction, video meeting and so on.This paper persents the method of the feature extraction and selection of face image based on Linear Discriminate Analysis(LDA). The task is the optimization combination of the feature vectors in the feature space after LDA transform by the improved artificial fish school algorithm(AFSA) based on chaos theory. The main task is as follows:Firstly, a CAFSA algorithm based on chaos theory is presented to resolve the problems of AFSA, such as poor performance of precision and low rate of convergence. Initializing population of fish with the chaos increases the diversity of fish and the chaos search makes fish to get rid of local minima and improve the efficiency. The simulation experiments show that the proposed method has more effective perfomances and robustness.Secondly, LDA fails to find the optimalest feature subspace for face classification in certain circumstances. Aiming at this problem, an improved LDA algorithm based on CAFSA is proposed in this paper. The novel method LDA-CAFSA selects the best feature from the feature subspace transformed by LDA through the stochastic global optimization ability of the CAFSA. The simulation experiments show that the proposed method is stronger than the basic LDA.As is stated, the chaos theory and AFSA algorithm with a combination of LDA method is accuracy and credibility algorithm in the extraction and selection of feature space. And this laids a good foundation for the follow study on the face recognition.