Research Of Face Recognition Based On Genetic Algorithm And Its Implementation On DSP

Face recognition technology has become one of the hot research topics in biometrics recognition, and it integrates computer graphics, digital image processing, computer vision, pattern recognition and artificial neural network. Face recognition technology has a broad prospect in public security and military security.Based on literature review and in-depth study of the face recognition system, this thesis designed a face recognition system based on genetic algorithm. The system used the Beijing Ruitai innovative company’ICETEK-DM6437-B as a hardware development platform. This system included video image light compensation, feature extraction, feature selection, classification and identification and it displayed the recognition results. The main achievements can be described as follows.(1) Image light compensation. In the real-time video acquisition system, light intensity has a great influence on the performance of the system. In this thesis, through comparative analysis of the experiment of three kinds of illumination compensation algorithm, this thesis drew the illumination compensation method.(2) Face detection and location. In the face detection stage, using the clustering of human skin color in YCbCr color space, this thesis chose skin color detection method to complete the candidate face region detection and used the feature parameters of face shape to exclude some non-face region.(3) Face feature extraction and selection. The thesis used the 8×8 block Local Binary Patterns(LBP) algorithm for feature extraction and extractd 640 dimensional feature vectors. This thesis used genetic algorithm for feature selection. According to the basic theoretical knowledge of genetic algorithm, this thesis presented an improved genetic algorithm. The main changes of the control parameters were the fitness function, selection operator, crossover probability and mutation probability. The experimental results showed that the improved genetic algorithm proposed in this thesis had better global convergence, the less iteration time and higher recognition rate than basic genetic algorithm.(4) Face recognition. This thesis used support vector machine classifier combined with nearest neighbor classifier to match the facial feature. In front, the nearest neighbor classifier was used for coarse classification to obtain two classes of the minimum distance with the test sample, and then it used the support vector machine fine classification.(5) On the ICETEK-DM6437-B-KIT hardware, this thesis achieved the function of the system. By using C language, it achieved the video face recognition in CCS.The experiment showd that recognition rate of the system designed in this thesis was beyond 90% and the system had a certain degree of stability and a strong practicality. It laid a good foundation for the further study of face recognition.