Face Recognition based on Opposition Particle Swarm Optimization and Support Vector machine

one of the most recently developed face recognition technique has utilized PSO-SVM, this method lacks in the initial phase of the PSO technique. That is in PSO; initially the populations are generated in random manner. Due to this random process, the population results may also be in random. Thus, it is not certain that this method will produce precise result. Hence to avoid this drawback, a modified face recognition method is proposed in this paper. Here, a new face recognition method based on Opposition based PSO with SVM (OPSO-SVM) is introduced. To accomplish the face recognition with our proposed OPSO-SVM, initially feature extraction process is carried out on the image database. In the feature extraction process, the efficient features are extracted and then given to the SVM training and testing process. In OPSO, the populations are generated in two ways: one is random population as same as the normal PSO technique and the other is opposition population, which is based on the random population values. The optimized parameters in SVM by OPSO efficiently perform the face recognition process. Two human face databases FERET and YALE are utilized to analyze the performance of our proposed OPSO-SVM technique and also this OPSO-SVM is compared with PSO-SVM and standard SVM techniques.