Face Recognition Based On Fuzzy Support Vector Machine

As a machine learning method, support vector machine can solve the non-linear, high dimension and other practical problem, it is the focus in the field of machine learning, and provides a valid path for face recognition.Because the sample usually has a lot of vague information, and the sparseness of the samples distribution are different, through studying some of the existing fuzzy support vector machine method, a fuzzy support vector machine method based on fuzzy K neighbors is presented in this paper. In this method, the sample mean is calculated,and the center of each calss is got; then the distance between the sample and the center is calculated, according to the distance sample's initial membership is got;by finding K neighbors for each sample point, the sample membership degree is calculated according to the fuzzy K nearest neighbor method,then the paper integrates initial membership degree with fuzzy K neighbors membership by a certain percentage that obtaines the final membership value of the sample.Combine with rice image detection, the validity of this method is verified.According to analyze the main component analysis and the two-dimensional principal component analysis regarding face image feature extraction, using two-dimensional features for the calculation of the membership and the principal component for support vector classification is proposed in this paper. The method combines the stabilization of two-dimensional principal component in reconstructing face image and the obviousness of the principal component to the reconstructed image local characteristics. In order to contrast with the sort results of two-dimensional characteristics, through introduction of matrix inner product, three types of two-dimensional characteristics kernel function are given. Experiments show that the method of this paper has a high classification accuracy for face recognition.