Abstract
The gait recognition is presented for human identification from a sequence of noisy silhouettes segmented from video. The proposed gait recognition algorithm gives better performance than the baseline algorithm because of segmentation of the object by using active shape model (ASM) algorithm. For the experiment, we used the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects, For realistic simulation we use various values for the following parameters; i) viewpoint, ii) shoe, iii) surface, iv) carrying condition, and v) time.
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Cho, W., Kim, T., Paik, J. (2007). Gait Recognition Using Active Shape Models. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_35
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DOI: https://doi.org/10.1007/978-3-540-74607-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
Online ISBN: 978-3-540-74607-2
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