Support Vector Machine-based Face Recognition Technology

Face recognition is one of the important branches of biometrics and it is also one ofthe most active fields of computer vision and pattern recognition. Compared with thedimension of the face image vectors, face recognition is a high dimensional and nonlinearsmall-sample problem. Support vector machine (SVM) can solve it without over fittingphenomenon. By introducing the kernel function, the nonlinear separate samples areprojected into a high dimensional space (so called "the linear separate feature space") inwhich the new separate problem is solved. SVM is originally designed for binaryclassification. How to effectively extend it for multi-classification problems is still anattractive research issue. This paper proposes an improved multi-classification structurewhich helps save time of training and testing and has high recognition rate.In this paper our main work is as follows:First we analyze many multi-classification methods based on SVM and those hyperplanes contributing to classification boundaries. Then we propose an improvedmulti-classification structure considering distance between classes in the feature space.The new algorithm reduces the number of hyper planes, and thus improves the trainingefficiency. Meanwhile we have improved the testing methods based on max win and treemethodology. We carry out experiments on UCI database to compare the improvedmethod with traditional ones.Then we investigate feature selection and abstraction method for human facerecognition and put forward a method based on wavelet decomposition and discretecosine transform (DCT). The low frequency sub-images are obtained by utilizingtwo-dimensional wavelet transform are stable with features independent on faceexpression and pose. Only a small set of DCT coefficients is retained as the featurebecause most of image energy focuses on a few DCT coefficients.Finally experiments are carried out ORL human face database to verify validity ofour algorithm. We compare these multi-classification methods in the aspects of trainingtime, the number of hyper planes, testing time and recognition rate. The conclusion is ourmethod is fit for real-time problems such as human face recognition.