Factorial hidden Markov models for gait recognition

Gait recognition is an effective approach for human identification at a distance. During the last decade, the theory of hidden Markov models (HMMs) has been used successfully in the field of gait recognition. However the potentials of some new HMM extensions still need to be exploited. In this paper, a novel alternative gait modeling approach based on Factorial Hidden Markov Models (FHMMs) is proposed. FHNlMs are of a multiple layer structure and provide an interesting alternative to combining several features without the need of collapse them into a single augmented feature. We extracted irrelated features for different layers and iteratively trained its parameters through the Expectation Maximization (EM) algorithm and Viterbi algorithm. The exact Forward-Backward alaorithm is used in the E-step of EM algorithm. The performances of the proposed FHMM-based gait recognition method are evaluated using the CMU MoBo database and compared with that of HMMs based methods.