Gender and Age Classification Based on Pattern of Human Motion Using Choquet Integral Agent Networks
Santoso Handri*,***, Kazuo Nakamura**, and Shusaku Nomura*
*Top Runner Incubation Center for Academia-Industry Fusion
**Department of Management and Information Systems Science Nagaoka University of Technology, 1603-1 Kamitomiokamachi, Nagaoka, Niigata 940-2188, Japan
***Multimedia Nusantara University, Tangerang — Indonesia
Automated human identification from their walking behavior is a challenge attracting much interest among machine vision researchers. However, the systems which are able to detect pedestrian attributes based on their walking behavior remain to be developed. Here, a soft computing approach to determine walking behavior based on motion imagery is studied as the basis for developing pedestrian safety information systems. Gender and age are classified based on motion pattern derived in experiments. At the front end, image and video processing was performed to separate foreground from background images. The widths of silhouette were analyzed using two-dimensional (2D) Fourier transformation to extract human motion features. Feature sub-sets were then selected to find salient, effective classification features. Finally, Choquet integral agent networks (CHIAN) with a competitive learning algorithm were employed to classify gender and age into its classes. The experimental results demonstrated capability of the proposed system to classify gender and age in highly accurately.
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