Abstract
Gait information is a useful biometric because it is a user-friendly property and gait is hard to mimic exactly, even by skillful attackers. Most conventional gait authentication schemes assume cooperation by the subjects being recognized. Lack of cooperation could be an obstacle for automated tracking of users and many commercial users require new gait identification schemes that do not require the help of target users. In this work, we study a new person-tracking method based on the combination of some gait features observed from depth sensors. The features are classified into three groups: static, dynamic distances, and dynamic angles. We demonstrate with ten subjects that our proposed scheme works well and the accuracy of equal error ratio can be improved to 0.25 when the top five features are combined.
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Mori, T., Kikuchi, H. (2019). Person Tracking Based on Gait Features from Depth Sensors. 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_65
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DOI: https://doi.org/10.1007/978-3-319-98530-5_65
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