Abstract：Aiming at the situation that many approaches for gait recognition are based on a single camera, an approach of gait recognition based on stereo vision is proposed. Firstly, 3D coordinates of human body contour are gotten by stereo matching. Then, 3D body contour descriptor (3D-BCD) is constructed to get the gait feature of human. The noise and glitch are eliminated by noise-eliminated method. Thus, manifold learning (Laplacian Eigenmaps) is used for dimensionality reduction. The nearest neighbor classifier (NN) and the nearest neighbor classifier about template (TNN) are used for classifying category. Finally, a series of experiment results on the stereo gait database of PRLABⅡ and the irregular test stereo gait dataset ExN proved out the high correct classification rate and the strong robustness of the proposed approach. And the approach is not related with the distance between camera and the walking path. Moreover, it has stronger robustness with the incomplete gait sequences, the changes of human behavior, the changes of apparels, and carrying a bag.
刘海涛，汪增福，曹洋. 基于流形学习的三维步态鲁棒识别方法[J]. 模式识别与人工智能, 2011, 24(4): 464-472.
LIU Hai-Tao, WANG Zeng-Fu, CAO Yang. 3D Robust Gait Recognition Based on Manifold Learning. , 2011, 24(4): 464-472.
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2011, Vol. 24 
