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. FHMMs 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 algorithm 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.

[1]  Dimitris N. Metaxas,et al.  Human Gait Recognition , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[2]  Yanxi Liu,et al.  Gait Sequence Analysis Using Frieze Patterns , 2002, ECCV.

[3]  Sudeep Sarkar,et al.  Studies on silhouette quality and gait recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[4]  Rama Chellappa,et al.  A framework for activity-specific human identification , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  Rama Chellappa,et al.  A hidden Markov model based framework for recognition of humans from gait sequences , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[6]  Matthew Brand,et al.  Coupled hidden Markov models for modeling interacting processes , 1997 .

[7]  Haihong Hu,et al.  Gait Recognition Using Hidden Markov Model , 2006, ICNC.

[8]  Yew-Soon Ong,et al.  Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I , 2005, ICNC.

[9]  Michael I. Jordan,et al.  Factorial Hidden Markov Models , 1995, Machine Learning.

[10]  Ralph Gross,et al.  The CMU Motion of Body (MoBo) Database , 2001 .

[11]  N. Komatsu,et al.  A gait recognition method using HMM , 2003, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[12]  Beth Logan,et al.  Factorial HMMs for acoustic modeling , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[13]  Beth Logan,et al.  Factorial Hidden Markov Models for Speech Recognition: Preliminary Experiments , 1997 .