Abstract
Human gait properties can be affected by various environmental contexts such as walking surface and carrying objects. In this paper, we propose a novel approach for individual recognition by combining different gait classifiers with the knowledge of environmental contexts to improve the recognition performance. Different classifiers are designed to handle different environmental contexts, and context specific features are explored for context characterization. In the recognition procedure, we can determine the probability of environmental contexts in any probe sequence according to its context features, and apply the probabilistic classifier combination strategies for the recognition. Experimental results demonstrate the effectiveness of the proposed approach.
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Han, J., Bhanu, B. (2005). Gait Recognition by Combining Classifiers Based on Environmental Contexts. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_43
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DOI: https://doi.org/10.1007/11527923_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
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