Face recognition method based on support vector machine and particle swarm optimization

Face recognition belongs to the problem of non-linear, which increases the difficulty of its recognition. Support vector machine (SVM) is a novel machine learning method, which can find global optimum solutions for problems with small training samples and non-linear, so support vector machine has a good application prospect in face recognition. In the study, the novel face recognition method based on support vector machine and particle swarm optimization (PSO-SVM) is presented. In PSO-SVM, PSO is used to simultaneously optimize the parameters of SVM. FERET human face database is adopted to study the face recognition performance of PSO-SVM, and the proposed method is compared with SVM, BPNN. The experimental indicates that PSO-SVM has higher face recognition accuracy than normal SVM, BPNN. Therefore, PSO-SVM is well chosen in face recognition.