الخلاصة

and AAPSO are the most recently developed face recognition techniques, in order to optimize the parameters of SVM. However, in order to increase the optimization, a combination between OPSO and AAPSO techniques has been proposed in this paper. The proposed technique is called Adaptive-Opposition particle swarm optimization (AOPSO). In AOPSO, the random values in the initial generation of the population in PSO is solved by OPSO and the randomization fixed values in the velocity coefficient is solved using AAPSO in the same time. Then, the proposed algorithm is used with support vector machine to find the optimal parameters of SVM. The performance of the proposed AOPSO method has been validated with two face images datasets, YALE and CASIA datasets. In the proposed method, we have initially performed feature extraction, followed by the recognition of the extracted features. In the recognition process, the extracted features have been employed for SVM training and testing. During the training and testing, the SVM parameters have been optimized with the AOPSO technique. The comparative analysis has demonstrated that, the AOPSOSVM proposed in this study has outperformed the existing PSO-SVM technique.