Abstract: 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 study, a new technique based on modified PSO, called (Modified PSO-SVM) is proposed to optimize SVM parameters. The proposed scheme utilizes modified PSO to seek the finest parameters of SVM two human face datasets: SCface, CASIAV5 and CMU Multi-PIE face datasets are used in the experiments. Then, a comparison is done with the PSO-SVM, OPSO-SVM and AOPSO-SVM and it showed promising results in terms of accuracy.