Gait Identification Based on Hidden Markov Model

This paper proposes a gait recognition method based on Continuous Hidden Markov Model with Mixture of Gaussians (G-CHMM). First, we initialize a Gaussian mix model for training image sequence with K-means algorithm, then train the HMM parameters using a Baum-Welch algorithm. Improved training process can be solved arithmetic overflow. Finally, the recognition is achieved by HMM. The experiments made on CASIA gait databases obtain comparatively high correction identification ratio and comparatively strong robustness for variety of bodily angle.