On applying support vector machines to a user authentication method using surface electromyogram signals

摘要

AbstractAt present, 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 detected over the skin surface, are generated by the electrical activity of muscle fibers during contraction. Muscle movement can be differentiated by analyzing the s-EMG. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In this paper, we introduced support vector machines (SVM) for improvement of the method of identifying gestures. A series of experiments was carried out to evaluate the performance of the SVM based method as a gesture classifier and we also discussed its security.