Research On Subspace Analysis Based Face Recognition Algorithm

Face recognition is the current well acknowledged and hot issues in the field of biometric identification, the key of which is to extract stable, reliable, distinct features. Subspace analysis of the feature extraction is main method due to simplicity and efficiency. In this paper, face recognition as the target, in order to resolve several feature extraction issues, a variety of feature extraction methods based on, subspace analysis are proposed, which combines with feature extraction mixture model and gets more reliable and effective features. The contributions of the dissertation can be noted as following.A modified method to extract features of face image based on Singular Value Decomposition (SVD) and Linear Discriminant Analysis (LDA) is proposed. Firstly, the mean image of all train samples is selected as a standard face image, and all the train samples are projected into the two orthogonal matrixes which come from the SVD of the standard face image. Then the left-top informations of projecting coefficient matrix are extracted as the primary feature. Finally, LDA is manipulated to extract the recognition feature. In this method, the problem of the equivalent basis space with SVD used into face recognition is amended,at the same time,class information of samples is added and problem of small sample with LDA is abolished. A new method to extract featuress of face image based on Singular Value Decomposition (SVD) and Kernel Discriminant Analysis (KDA) is proposed. Non-linear feature is accordingly extracted.Experiments are conducted on-ORL and CAS-PEAL databases, the results of which have indicated the method is effectiveSubspace method combining.2DPCA with 2DLDA is proposed for face recognition.This method performs 2DLDA or 2DPCA twice, one is 2DPCA in horizontal direction and the other is 2DLDA in vertical direction. The,advantage of this-method over the standard (2D)2PCA and (2D)2LDA method is that the former not only includes the class information of samples but also eliminates the image matrix correlation of the row and column. At the same time,it seeks both the column and row information, the dimension also is less than standard 2DLDA and 2DPCA. Experiments on ORL and FERET database indicate that this method is effective. This paper targets the solution of all the sub-image choose the same mapping matrix and do not discriminate facial local information in Module single-projecting feature extraction method, Module multi-projecting feature extraction method constructs module multi-mapping matrix, making different sub-image projcet different matrix. In this way, the rat of recognition is enhanced by making use of local facial information effectively and we have done experiment in ORL face database and proved its effectivenessA method of feature fusion face recognition based on wavelet pack transform and principal component analysis is proposed. Firstly, each face image was decomposed into sixteen sub-image by using two-dimensional discrete wavelet transform, and then PCA was using to extract the feature of low frequency sub-image. The selection of the high frequency sub-images includes abundant of human face information to be combined. Then, PCA was using to extract the feature of high frequency fusion image. All the extracted features were further fused and used for face classification. The experiments on the ORL face database and YaleA face database indicate that the method can reach a higher recognition rate.A new face image feature extraction and recognition algorithm based on kernel discriminant locality presercing projections was proposed, in which face images are projected into high dimensional feature spaces by using kernel trick. Then, in kernel space a new objective function is constructed with the face manifold local structure information is combined with the labels'information. The advantage of the method is not only the face manifold is preserved, but also the label information is being used, at the same time, non-linear feature is abstracted. Experiments have been done on ORL and UMIST, the experimental results showed that KDLPP is powerful than LPP, DLPP and KLPP.Two-dimensional locality preserving projection (2DLPP) extracts features only in the horizontal direction of the image and feature's dimension is high. An alternative two-dimensional locality preserving projection (A2DLPP) method is given, and then double two-dimensional locality preserving projection((2D)2LPP) is designed. The algorithm executes 2DLPP separately from both horizontal and vertical directions, so that the features of the horizontal and vertical dimensions have been an effective reduction. Experimental results show that, both the recognition rate and recognition time of (2D) 2LPP are superior to 2DLPP and A2DLPP.