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.