Support vector machine can determine the global finest solutions in many complicated problems and it is widely used for human face classification in the last years

Support vector machine can determine the global finest solutions in many complicated problems and it is widely used for human face classification in the last years. Nevertheless, one of the main limitations of SVM is optimizing the training parameters especially when SVM used in face recognition domains. Various methodologies are used to deal with this issue, such as PSO, OPSO, AAPSO and AOPSO. Nevertheless, there is a room of advancements in this kind of optimization process. Lately, an improved version of PSO is developed which is called Modified PSO. In this paper, a new technique based on Modified PSO called (Modified PSO-SVM) is proposed to optimize the parameters of SVM. The proposed scheme utilizes Modified PSO to select the optimal parameters of SVM. Two human face datasets: SCface, CASIAV5 and CMU Multi-PIE face datasets are used in the experiments. Then, the proposed technique is compared with the PSO-SVM, OPSO-SVM and AOPSO-SVM and it showed promising results in terms of accuracy.

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