Study Of Face Recognition Technology

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 proposed based on Principle Component Analysis . And the corresponding solutions are given. The work including:(1) Based on algebraic features of the images, this paper first introduced the PCA-Based face recognition algorithm. Some research have done on the selection of the eigenvector which used to create the eigenspace ,the distance measure methods and the selection of the training set. 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) This paper proposed a wavelet transform based recognition algorithm. The computing time of the traditional PCA-Based face recognition algorithm is very large. So the wavelet transform method is used to do the preprocessing. The method this paper proposed is different from the traditional wavelet transform method used in face recognition. In the proposed method, the detail subimage and the vertical direction subimage are combined as the PCA algorithm's training set and the probe image. The experiments results present that the speed of the training and recognition is six times faster than the traditional PCA algorithm introduced in the precious chapter while the correct recognition rate is almost equal to the traditional wavelet transform based face recognition method.(3) PCA algorithm can't effectively use the training samples to improve the recognition rate while each class has much training samples. This paper proposed a method called PCA&SVM based on each class. After present PCA on each class, their eigenspace are got to train the corresponding Support Vector Machines respectively. Then the probe images are tested by the SVM. Because SVM has the strong ability of segmenting high dimension data and PCA can distill the eigenspace effectively, the result of the experiment is satisfied.