At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, are detected over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. We proposed a method that uses a list of gestures as a password in the previous study. In this paper, results of experiments are presented that was carried out to investigate the performance of the method identifying gestures from s-EMG signals using support vector machines (SVM). An experiment to identify users from s-EMG signals was carried out at the same time. The performance of SVM as a classifier of our method was also discussed according to the results.