Gait Recognition Based On the Feature Fusion
A gait recognition algorithm is proposed that fuses motion and static features of sequences of silhouette images--the wavelet moment and the widths capture the motion and static characteristic of gait. A subspace transformation, Principal Component Analysis(PCA),is applied to process the spatial templates. It aims essentially at reducing data dimensionalities. Finally, nearest neighbor classifier is adopted to recognize subjects. Experimental results show that the method is efficient for human identification, and has a recognition rate of around 88％ on the CASIA data set, furthermore, the performance is compared with other algorithms.
gait recognition Wavelet Moment contour width Principal component analysis
Zhu Jinghong Fang Shuai Fang Jie Wang Yong
School of Computer and Information, Hefei University of Technology, Hefei 230009,china School of Computer and Information, Hefei University of Technology, Hefei 230009,china HeFei Teacher
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5449-5452
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）