Face Recognition Method Based On The Algebraic Characteristics Of Study

The technology of face recognition is an active subject in the area of pattern recognition. There are broad applications in the fields of law, business etc. For the particularity of the face image, face recognition is also the very difficult problem. There is still much work to do. In this paper, some of face recognition algorithms are probed based on algebraic features of the images. And the corresponding solutions are given.The work including:(1) Eigenfaces based on PCA and the selection of the eigenvectorBased on algebraic features of the images, this paper first introduced the PCA-Based face recognition algorithm. We emphasized the selection of the eigenvector which used to create the eigenspace. Considering the recognition performance and the computation time, this paper proposed a method using to select the different number of the eigenvector in allusion to different training datasets. In the end, the ORL and Yale dataset are used for the experiments.(2) Subspace LDA algorithm based on Fisher linear discriminantThis paper introduced Subspace LDA algorithm based on Fisher linear discriminant for the PCA-Based face recognition algorithm can't utilize the class information of train data. Firstly this paper introduced the principle of Fisher linear discriminant function, and then present the Subspace LDA algorithm which project the data that is in high dimension space to eigenspace that is in low space and then maximize discriminant coefficient. The result proved Subspace LDA algorithm eliminates some effection because of illumination or expression. This algorithm is better than PCA-Based face recognition algorithm.(3) Improved LDA algorithm based on pairwise weighted Fisher CriteriaThe application of Fisher function can have the best performance in classification relative to Fisher criterion. But this result is not the best result relative to Bayes error or risk. This paper introduces a face recognition method based on pairwise weighted Fisher Criteria. By constructing weighted between-class scatter matrix, the classes that are closer to one another are likely to have a greater confusion and should be given a greater weightage. Moreover, the classification error rate is related to the Bayes error by selecting appropriate weighting function. Experiments show that thenew method is useful in the classification because higher accuracy was achieved.