Human gait based gender identification system using Hidden Markov Model and Support Vector Machines
- 1 May 2015
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in International Conference on Computing, Communication & Automation
Abstract
The paper presents an approach towards human gender recognition system. The Silhouettes from Center for Biometrics and Security Research (CASIA) gait database are segmented in order to identify major body points and to generate corresponding point-light display. The features such as two dimensional coordinates of major body points and joint angles are extracted from the point-light display. The features are classified using Hidden Markov Model (HMM) and Support Vector Machines (SVM). The study yields a recognition rate of 69.18% and 76.79% with 100 subject data using HMM and SVM respectively. There has been a significant improvement in recognition accuracy using joint angles as the features.Keywords
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