Research On Block Kernel Independent Component Analysis Of Face Recognition Method

A face recognition method based on the column-block and kernel independent components analysis is proposed combining with the kernel independent component analysis and the thought of image divided by column in this paper. First of all, the face image matrix are divided into blocks by column according to this method. Then kernel independent components analysis could be directly used to extract the feature of face image and recognition in the new eigenspace constructed by all the blocks. The Experimental results of show that this method can solve the defects of small high-dimensional and number samples in some degree through reducing the dimension of samples and increasing the number of samples. This method can extract the local feature of face image more effectively than the traditional kernel independent components analysis, besides the recognition performance of this method is better than the traditional kernel independent components analysis.Another face recognition method based on the ranks-block and kernel independent components analysis is proposed through improvement of the above method. First of all, the face image matrix are divided into blocks by columns and rows according to this method, and these blocks are mixed to construct the new eigenspace. Then kernel independent components analysis used twice followed by rows and columns in the new eigenspace to obtain the left-unmixed matrix and right-unmixed matrix. At last we can extract the feature of face image and recognize according to the left-unmixed matrix and right-unmixed matrix. The Experimental results of show that this method can eliminate the correlation between the samples through the twice use of kernel independent components analysis, besides the recognition performance and the robustness of this method is better than the first method.