The Research Of Mainstream Feature Extraction And Integration For Face Recognition

Face recognition is a top research subject in the computer visual field. It has a very high scientific value and broad application prospects, and feature extraction is one of the most critical aspects in the area of face recognition, it affects the accuracy of identification directly.This article describes three feature extraction methods for face image based on algebraic feature. They are principal component analysis,Fisher linear discriminant analysis,maximum scatter difference discriminant analysis. This article also analysis the features and disadvantages of the three methods.According to the common needs of the average sample solution in traditional linear analysis methods, this article proposes two improved methods. After a face database in the standard experiments, it proves that both the two way can remove the influence of Average samples to Interference samples, also can improve the recognition accuracy.This article also tries to use the canonical correlation analysis to fuse the features of the improved Fisher linear discriminant analysis and maximum scatter difference discriminant analysis, and through the experiments, it proved that the way we have tried has the high recognition rate, achieved the fusion results.