A novel algorithm of gait recognition

Rapidly development in the realm of biometrics in recent years results in the unprecedented growth with the number of researchers and interesters. As one of the biometrics, gait recognition has many advantages such as from a distance, lower quality video, hard to disguised comparing with others. However, nearly all studies on gait recognition are 2D methods based on analysis of image sequences captured by a monocular camera. This paper presents a novel 3D method for automatic gait recognition by analyzing the changes of a 1D silhouette signal. Binarized silhouette of a moving individual is firstly achieved. Secondly contour is extracted for next stereo matching. Afterward 3D contour is obtained through a step of contour based stereo matchin. Then, stereo silhouette vector (SSV) is extracted from 3D contour and transformed to a 1D silhouette signal as the stereo gait feature (SGF). K-L transform is applied for reducing the dimensionality of feature and the complexity of computing. Finally, kNN and NN are applied to gait classification and recognition. A stereo gait database named PRLAB is established for gait recognition based on stereo vision. Experimental results on PRLAB showed the efficiency and robustness of our algorithm.