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Gait recognition based on model-based methods and deep belief networks

Gait recognition based on model-based methods and deep belief networks The sensitivity to illumination variations, pose, gender, age, clothing and any another source of changes, can be one of the most important challenges, in gait recognition system. In this paper, we adopt many approaches to extract signatures of human body (static model) using a model-based method, such as static body parameters, ellipse-fitting and robust shape coding. To reduce the dimension of this features set, a principal component analysis (PCA) technique is employed. Then, a deep belief networks classifier is used to classify the gait signatures. The performance of the deep belief network (DBN) is superior to other classifiers such as k-nearest neighbour (KNN) and dynamic times warping (DTW). The comparison is performed for viewpoint changes, clothing and carrying conditions. The proposed approach has been validated on the gait database B. Keywords: biometrics; gait; model-based; model free; feature extraction; principal component analysis; PCA; k-nearest neighbour; dynamic times warping; DTW; deep belief network; DBN. Reference to this paper should be made as follows: Benouis, M., Senouci, M., Tlemsani, R. and Mostefai, L. (2016) `Gait recognition based on model-based methods and deep belief networks', Int. J. Biometrics, Vol. 8, Nos. 3/4, pp.237­253. Copyright © 2016 Inderscience Enterprises Ltd. Biographical notes: Mohamed http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Gait recognition based on model-based methods and deep belief networks

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Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2016.082598
Publisher site
See Article on Publisher Site

Abstract

The sensitivity to illumination variations, pose, gender, age, clothing and any another source of changes, can be one of the most important challenges, in gait recognition system. In this paper, we adopt many approaches to extract signatures of human body (static model) using a model-based method, such as static body parameters, ellipse-fitting and robust shape coding. To reduce the dimension of this features set, a principal component analysis (PCA) technique is employed. Then, a deep belief networks classifier is used to classify the gait signatures. The performance of the deep belief network (DBN) is superior to other classifiers such as k-nearest neighbour (KNN) and dynamic times warping (DTW). The comparison is performed for viewpoint changes, clothing and carrying conditions. The proposed approach has been validated on the gait database B. Keywords: biometrics; gait; model-based; model free; feature extraction; principal component analysis; PCA; k-nearest neighbour; dynamic times warping; DTW; deep belief network; DBN. Reference to this paper should be made as follows: Benouis, M., Senouci, M., Tlemsani, R. and Mostefai, L. (2016) `Gait recognition based on model-based methods and deep belief networks', Int. J. Biometrics, Vol. 8, Nos. 3/4, pp.237­253. Copyright © 2016 Inderscience Enterprises Ltd. Biographical notes: Mohamed

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2016

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