15 May 2014 Feature selection gait-based gender classification under different circumstances
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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.
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Azhin Sabir, Azhin Sabir, Naseer Al-Jawad, Naseer Al-Jawad, Sabah Jassim, Sabah Jassim, "Feature selection gait-based gender classification under different circumstances", Proc. SPIE 9139, Real-Time Image and Video Processing 2014, 91390A (15 May 2014); doi: 10.1117/12.2052586; https://doi.org/10.1117/12.2052586


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