Decision Fusion Gait Recognition Based on Bayesian Rule and Support Vector Machine

Abstract:

To distinguish people’s identities, the information is normally included in one gait periodic sequence image. First, the gait energy image for feature extraction of wavelet moments was constructed. After boundary unwrapping, the gait silhouette boundary was extracted and principal component analysis (PCA) was use to obtain its compressed contour features. Then nearest neighbor classifier and support vector machines were applied for classification of these two features. Finally, support vector machine (SVM) on Bayesian rule were used to complete gait recognition with information fusion of different features. The method is evaluated on the National Laboratory of Pattern Recognition (NLPR) gait database and the correct recognition rate is relatively high. The experimental results show that the proposed method has good recognition performance.

• Periodical: Applied Mechanics and Materials (Volumes 411-414)
• Pages: 1287-1290
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.411-414.1287
• Online since: September 2013
• Authors: Hong Zheng, Kai Zhang
• Keywords: Bayesian Rule, Gait Recognition, Information Fusion, Principal Component Analysis (PCA), Support Vector Machine (SVM), Wavelet Moments

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