Article: Gait recognition based on model-based methods and deep belief networks Journal: International Journal of Biometrics (IJBM) 2016 Vol.8 No.3/4 pp.237 - 253 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Mohamed Benouis; Mohamed Senouci; Redouane Tlemsani; Lotfi Mostefai

Addresses: Computer Science Department, University of Oran 1 Ahmed Ben Bella, Algeria Street El senia el mnouer bp 3100 Oran, Algeria ' Computer Science Department, University of Oran 1 Ahmed Ben Bella, Algeria Street El senia el mnouer bp 3100 Oran, Algeria ' Telecommunication Department, INTTIC, ORAN, Algeria Street El senia el mnouer bp 3100 Oran, Algeria ' Electrical Engineering Department, University of Saida, Algeria Street El naser bp 20000 Saida, Algeria

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 recognition; model-based methods; model free; feature extraction; principal component analysis; PCA; k-nearest neighbour; kNN; dynamic time warping; DTW; deep belief networks; DBN; human body model; static modelling; body parameters; ellipse fitting; robust shape coding.

DOI: 10.1504/IJBM.2016.082598

International Journal of Biometrics, 2016 Vol.8 No.3/4, pp.237 - 253

Received: 17 Jun 2016
Accepted: 03 Oct 2016
Published online: 02 Mar 2017 *

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

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