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Proceedings Paper

Feature selection gait-based gender classification under different circumstances
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Paper Abstract

This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

Paper Details

Date Published: 15 May 2014
PDF: 9 pages
Proc. SPIE 9139, Real-Time Image and Video Processing 2014, 91390A (15 May 2014); doi: 10.1117/12.2052586
Show Author Affiliations
Azhin Sabir, The Univ. of Buckingham (United Kingdom)
Naseer Al-Jawad, The Univ. of Buckingham (United Kingdom)
Sabah Jassim, The Univ. of Buckingham (United Kingdom)


Published in SPIE Proceedings Vol. 9139:
Real-Time Image and Video Processing 2014
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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