Gait analysis for human walking paths and identities recognition

In this paper, we propose a gait analysis method which extracts the dynamic and static information from human walking for walking path and identity recognition. First, we utilize the periodicity of swing distances to estimate the gait period for each gait sequence. For each gait cycle, we extract the dynamic information by analyzing the statistic histogram of motion vectors and static information using Fourier descriptors. The extracted information is transformed into lower dimensional embedding space to represent the subject. Given a test feature vector, the nearest neighbor classifier is applied to compare with the feature vectors in the gait database for human object identification. The proposed algorithm is evaluated on the CASIA gait database, and the experimental results demonstrate this new system achieves a high recognition rate.