The Study Of Human Face Recognition Based On Some Algebraic Features

Face recognition is hot in the area of pattern recognition and image process recently, and the research for it will be benefit for the progress of pattern recognition and information security. Feature extraction is one of the foundational problems in the research of pattern recognition, and it is the key to the problem of image identification. In the field of face recognition, the method based on the algebraic features of the images has been received extensive attention owing to its easily computation and effectiveness. Now the face recognition algorithms on the basis of algebraic features of the images has become the mainstream technology for feature extraction and face recognition. In this paper, combined with several methods of face recognition algorithms based on algebraic features of the images, some problems in them has been probed, and the corresponding solutions are given.This paper includes the following parts:(1) There are brief introduction and clearly analyses of the theories of SVD,PCA,FLD and 2DPCA, which are used widely in face recognition that based on algebraic features. Through the experiments again and again, we find although every method has it's own advantages but also has some disadvantages, so it can't get the perfect result. In this paper, the testing results of each method are given after the experiments of recognition in ORL face image base, and the advantages and disadvantages of each method are compared.(2) After introducing the feature extraction methods of SVD and PCA respectively, a face recognition method based on the integration of SVD and PCA is put forward. Theoretically, fusion of different data or classifiers can achieve better performance when they are independent or complementary. SVD and PCA are clearly complementary to one another. PCA is the best one in image expression, but one of drawbacks of PCA-based method is that PCA is sensitive to translation, rotation and other geometric transforms. Contrary to PCA, SVD has the merit of invariance to translation, rotation and other geometric transforms. By combining these two methods, it is expected that better recognition performance can be obtained. Experiment results from ORL face database demonstrate that the proposed method can indeed improve face recognition rate.(3) 2DPCA is proposed based on PCA and it have more advantages than PCA for it's higher capacity of face recognizing. FLD makes the relation of the classes and the relation of the elements in one class clearly and it also make the elements extracted more unattached. According to this character of FLD, this paper has fused the method of FLD into the 2DPCA in the process of extracting the features, and also shows another method called LDA+2DPCA, hoping to achieve a far more effective capacity of recognition. The result of the test shows that the methods being introduced have achieved the expected fusion effect.