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Layered time series model for gait recognition

Layered time series model for gait recognition

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  • Author(s): C. Chen 1 ; J. Liang 1 ; H. Zhao 1 ; H. Hu 1 ; L. Jiao 2
    • View affiliations
    • Affiliations: 1: Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, People's Republic of China
      2: Intelligent Perception and Image Understanding, Key Lab of Ministry of Education of China, School of Electronic Engineering, Xidian University, Xi'an, People's Republic of China
  • Source: Volume 46, Issue 6, 18 March 2010, p. 412 – 414
    DOI:  10.1049/el.2010.2738 , Print ISSN 0013-5194, Online ISSN 1350-911X
© The Institution of Engineering and Technology
Published

A new gait recognition algorithm, the layered time series model (LTSM), is proposed. LTSM is a two-level model which combines the dynamic texture model (DTM) and the hidden Markov model (HMM). A gait cycle is divided into several temporally adjacent clusters and gait features of each cluster are modelled by the DTM. The HMM is built to describe the relationship among the DTMs, which are regarded as hidden states. Experiment results show that the proposed model outperforms other approaches in terms of recognition accuracy.

Inspec keywords: hidden Markov models; time series; biometrics (access control); pattern recognition; gait analysis; image texture

Other keywords: biometrics; gait recognition; dynamic texture model; hidden Markov model; gait pattern; layered time series model

Subjects: Markov processes; Image recognition; Markov processes; Optical, image and video signal processing

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