Gait recognition using hidden Markov model

Gait-based human identification is a challenging problem and has gained significant attention. In this paper, a new gait recognition algorithm using Hidden Markov Model (HMM) is proposed. The input binary silhouette images are preprocessed by morphological operations to fill the holes and remove noise regions. The width vector of the outer contour is used as the image feature. A set of initial exemplars is constructed from the feature vectors of a gait cycle. The similarity between the feature vector and the exemplar is measured by the inner product distance. A HMM is trained iteratively using Viterbi algorithm and Baum-Welch algorithm and then used for recognition. The proposed method reduces image feature from the two-dimensional space to a one-dimensional vector in order to best fit the characteristics of one-dimensional HMM. The statistical nature of the HMM makes it robust to gait representation and recognition. The performance of the proposed HMM-based method is evaluated using the CMU MoBo database.