Gait Recognition Using Variance of Dynamic Region

To solve the problem that the clothes variance or taking goods may affect the result of gait recognition,a new gait recognition method based on variance of dynamic region is proposed in this paper. Firstly,through the background subtraction and shadow elimination,human motion silhouettes are obtained,which will be normalized in terms of location and scale. Next,the dynamic regions are obtained using gait energy image and threshold segmentation,and gait feature is extracted from the dynamic region using the sector region distance transform. At last,maximum entropy markov Model is used to model the gait sequences of each people and implements recognition based on probability graph. The method is evaluated for the CASIA gait database and receives comparatively high correct recognition rate. The experimental results show that our approach is robust in the case of clothes variance and taking goods.