Abstract
Gait is a very important biometric technology for human recognition. Gait feature can be divided into two categories: static and dynamic. Many previous works argue that, although motion reflects the essential nature of gait, the recognition performance based purely on the motion feature is limited. The root cause of the limited performance is however not yet to understand. In this paper, we study the gait recognition with motion feature by Kinect and show that, with a novel representation, the motion feature is still effective to distinguish the gaits from different human beings. In particular, relative distance-based motion features are proposed, which are extracted without calculating the gait cycle. Experimental results show that the accuracy of recognition with relative motion features is up to 85 %, which is comparable to that of static features. By combining the relative motion features and the static ones, the accuracy is above 95 %.
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This work was supported in part by National High Technology Research and Development Program of China under No. 2012AA012706.
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Yang, K., Dou, Y., Lv, S., Zhang, F. (2015). Exploring Relative Motion Features for Gait Recognition with Kinect. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_62
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DOI: https://doi.org/10.1007/978-3-319-26561-2_62
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