The Research Of Linear Subspace Method For Face Recognition

Since the machine recognition of faces can be widely applied on many aspects in the society,the researches on the face recognition technique have been the spotlight of the research in pattern recognition field. From the view of extracting face features, we discussed several new facerecognition methods in the current, and improved them.Furthering the method based on image principal component analysis. A two-phase algorithm of image projection discriminant analysis is proposed in this paper. The discriminant method is composed of feature extraction based on maximum margin criterion (MMC) and Fisher discriminant analysis (FDA).Furthermore, a new technique called 2-directional 2-dimensional maximum margin discriminant analysis ((2D)2MMDA) is proposed for face image recognition. The proposed (2D)2MMDA method works image matrix in the row direction and in the column direction simultaneously for feature extraction. The experimental results on ORL face databases indicate that the proposed (2D)2MMDA method has the advantage of higher recognition rate, lesser memory requirements and better computing performance than the 2D-PCA and (2D)2PCA method.This article through the Modular PCA thought, Modular PCA is firstly used on the original images to get lower dimensional patterns corresponding to the original images. Then the well- known PCA-faces method is followed on the lower dimensional patterns to finish the pattern classification. then this article proposed the Modular MMC feature extraction method. it roughly thought similar to Modular PCA,but it considered the class information of training sample. The experimental results on ORL face databases indicate that the proposed methods had the stronger effective capability.Independent Component Analysis(ICA) is an important subspace method for face recognition besides LDA. In this paper, we discover the speciality of ICA method when used for small class problem. through the theoretical analysis and the simulation experiment, we explored the theory essence of the Independent Component Analysis and the recognition performance capability. In this foundation, the author carried on the exploration to the Independent Component Analysis on the non-linear expansion and apply in face recognition , it is verified to be effective in the application of face recognition in large face database.