Abstract
We focus on recognizing persons according to the way they walk. Our approach considers a human movement as a set of trajectories formed by specific anatomical landmarks, such as hips, feet, shoulders, or hands. The trajectories are used for the extraction of distance-time dependency signals that express how a distance between a pair of specific landmarks on the human body changes in time as the person walks. The collection of such signals characterizes a gait pattern of person’s walk. To determine the similarity of gait patterns, we propose several functions that compare various combinations of extracted signals. The gait patterns are compared on the level of individual walk cycles in order to increase the recognition effectiveness. The results evaluated on a 3D database of walking humans achieved the recognition rate up to 96 %.
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Sedmidubsky, J., Valcik, J., Balazia, M., Zezula, P. (2012). Gait Recognition Based on Normalized Walk Cycles. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_2
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DOI: https://doi.org/10.1007/978-3-642-33191-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33190-9
Online ISBN: 978-3-642-33191-6
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