Abstract
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 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 caracteristics, we proposed a method that uses a list of gestures as a password in the previous study. In this paper, we employed support vector machines and attempted to improve the gesture recognition method by introducing correlation coefficient and cross-correlation. A series of experiments was carried out in order to evaluate the performance of the method.
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Acknowledgements
This work was supported by JSPS KAKENHI Grant Numbers JP17H01736, JP17K00186.
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Yamaba, H., Aburada, K., Katayama, T., Park, M., Okazaki, N. (2019). Evaluation of User Identification Methods for Realizing an Authentication System Using s-EMG. In: Barolli, L., Kryvinska, N., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-98530-5_64
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DOI: https://doi.org/10.1007/978-3-319-98530-5_64
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