Human Identification Based on Gait Sequences
基于步态序列图像的身份确认

This paper explores the dynamic feature of human gait extracted by hidden Markov model(HMM),which is used for identifying people. At first,an improved angular vector representation is proposed for binarized human images in a gait sequence so that every image is turned into a one-dimension vector. Then these vectors act as feature vectors to build and train HMMs which are the final identifying tools for each person based on input gait sequences. The improved angular vector is equipped with better robustness against segment errors,so it is suitable for imperfectly segmented silhouettes. It is also easy to scale up or down,thus scarcely vulnerable to the change of walking direction and distance from data-collecting camera. HMM models not only the dynamic characteristic of gait but also the relation between images in the same sequence. Besides,it can guarantee a high-speed operation which carries out the whole process within 2min. The experiments on Soton and NLPR database yield encouraging correct identifying rate of 100% and 85%,which demonstrates the effectiveness of this method.