Face Recognition Based On Rough Sets And Support Vector Machines

Face recognition technology involves many fields, including image processing, pattern recognition ,artificial intelligence and so on., which has become challenging subjects in computer vision and pattern recognition fields. So it has wide applications prospect in many fields, such as in department of national security and bank password system.The generalization ability of SVM algorithm is decreased due to over-learning, in the paper, a hybrid classification algorithm based on margin of rough sets and V-support vector machine(RSM-V-SVM) was proposed which combining imprecise data ability of rough set. Firstly, the algorithm get the boundary set using uncertainty properties of margin region of rough set theory before training, which substitute the original inputs as a training subset, and the size of the training set was shorten. Then, the concept of upper and lower approximation set of rough set was introduced for improving the V-SVM, which was based on the V-SVM algorithm. Experimental results show that the algorithm can't influence recognition rate and shorten training time improved V-SVM while keeping the speed of classification and the performance of generalization. Algorithm attribute reduction (AR) of rough set theory was introduced to reduce data's features, so the computation and the time complexity is decreased.In face recognition process, Extract the features of face images with the kernel principal component analysis (KPCA) and AR, then get the eigenvectors of face images as the input of RSM-V-SVM algorithm, produce RSM-V-SVM classifier last. Face images can be recognized with RSM-V-SVM classifier. The experiments on ORL face database show that face recognition process of combined feature extraction and RSM-V-SVM has strong generalization performance while keeping recognition rate.