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Proceedings Paper

Model based and model free methods for features extraction to recognize gait using fast wavelet network classifier
Author(s): Aycha Dorgham; Tahani Bouchrika; Mourad Zaied
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Paper Abstract

Human gait is an attractive modality for recognizing people at a distance. Gait recognition systems aims to identify people by studying their manner of walking. In this paper, we contribute by the creation of a new approach for gait recognition based on fast wavelet network (FWN) classifier. To guaranty the effectiveness of our gait recognizer, we have employed both static and dynamic gait characteristics. So, to extract the static features (dimension of the body part), model based method was employed. Thus, for the dynamic features (silhouette appearance and motion), model free method was used. The combination of these two methods aims at strengthens the WN classification results. Experimental results employing universal datasets show that our new gait recognizer performs better than already established ones.

Paper Details

Date Published: 8 December 2015
PDF: 6 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987510 (8 December 2015); doi: 10.1117/12.2228498
Show Author Affiliations
Aycha Dorgham, Univ. of Sfax (Tunisia)
Tahani Bouchrika, Univ. of Sfax (Tunisia)
Mourad Zaied, Univ. of Sfax (Tunisia)

Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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