A method of combining Bayes rule with HMM in gait recognition

This paper presents a framework of combining Bayes rule with Hidden Markov Model (HMM) to recognize human identification by gait, indoors. First, the monitored human motion is detected mainly by a three-frame differencing algorithm. Then, a curve of centroid on the object's motion can be acquired. The curve is transformed into the observation sequence of its corresponding HMM by adaptive filtering, median filtering, line fitting, rotating equivalently, normalizing, nearest neighbor clustering, and cycle extracting in turn. During the process of training the HMM with Baum-Welch algorithm, the original parameters of matrix B is modified statistically by Viterbi algorithm, making the final trained model approximate global optimization further. And the prior knowledge in the Bayes rule is also acquired from relative learning. Lastly, the observation sequence is used to recognize the human identification by means of combining Bayes rule with the Forward-Backward algorithm in trained HMM. In the end, the performance of the method is illustrated by the videos of the CASIA Gait Database, the result acquires comparatively higher recognition rate and is robust for the objects' clothes to a certain extent. The framework of this paper is fitting for monitoring indoors. The type of the gallery monitored is straight and the sight angle for object is between 0° and 180°.