关键词: 步态识别, Kinect, 骨骼模型, 质心, 频谱分析, 协同表示

Abstract: Aiming at the problem of the influence which is easily caused by clothing and carrying object on existing gait recognition, this paper proposes a novel skeleton model-based method for gait recognition using Kinect. The center of mass of 3D gait skeleton model is obtained and combined with the activity information of the body limbs which have rich movement characteristics in the gait cycle to form the dynamic and static features respectively. Regarded as a periodic signal, the spectrum of dynamic features is analyzed by wavelet transform and discrete Fourier transform with Gaussian filter. This processing can eliminate the effect of external interference and enhance the difference among features. After projecting into dissimilarity space via dynamic time warping and matching by collaborative representation classification, the gait features are normalized, weighted and fused. Finally, the nearest neighbor algorithm is adopted to classify. Compared with sparse representation recognition, the experimental results show that the proposed method can improve recognition effect and provide reliable theoretical basis for gait recognition in the field of identity authentication.

Key words: gait recognition, Kinect, skeleton model, center of mass, spectrum analysis, collaborative representation