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Gait Feature Fusion using Factorial HMM
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Author(s): Jimin Liang (Xidian University, China), Changhong Chen (Xidian University, China), Heng Zhao (Xidian University, China), Haihong Hu (Xidian University, China)and Jie Tian (Xidian University, China)
Copyright: 2010
Pages: 18
Source title:
Behavioral Biometrics for Human Identification: Intelligent Applications
Source Author(s)/Editor(s): Liang Wang (University of Bath, United Kingdom)and Xin Geng (Southeast University, China)
DOI: 10.4018/978-1-60566-725-6.ch009
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Abstract
Multisource information fusion technology offers a promising solution to the development of a superior classification system. For gait recognition problem, information fusion is necessary to be employed under at least three circumstances: 1) multiple gait feature fusion, 2) multiple view gait sequence fusion, and 3) gait and other biometrics fusion. Feature concatenation is the most popular methodology to integrate multiple features. However, because of the high dimensional gait data size and small available number of training samples, feature concatenation typically leads to the well-known curse of dimensionality and the small sample size problems. In this chapter, we explore the factorial hidden Markov model (FHMM), an extended hidden Markov model (HMM) with a multiple layer structure, as a feature fusion framework for gait recognition. FHMM provides an alternative to combining several gait features without concatenating them into a single augmented feature, thus, to some extent, overcomes the curse of dimensionality and small sample size problem for gait recognition. Three gait features, the frieze feature, wavelet feature, and boundary signature, are adopted in the numerical experiments conducted on CMU MoBo database and CASIA gait database A. Besides the cumulative matching score (CMS) curves, McNemar’s test is employed to check on the statistical significance of the performance difference between the recognition algorithms. Experimental results demonstrate that the proposed FHMM feature fusion scheme outperforms the feature concatenation method.
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