Abstract
At 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.
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Acknowledgements
This work was partially supported by JSPS KAKENHI Grant number JP17K00186.
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This work was presented in part at the 22nd International Symposium on Artificial Life and Robotics, Beppu, Oita, January 19–21, 2017.
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Yamaba, H., Kurogi, T., Aburada, K. et al. On applying support vector machines to a user authentication method using surface electromyogram signals. Artif Life Robotics 23, 87–93 (2018). https://doi.org/10.1007/s10015-017-0404-z
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DOI: https://doi.org/10.1007/s10015-017-0404-z