SICE Journal of Control, Measurement, and System Integration
Online ISSN : 1884-9970
Print ISSN : 1882-4889
ISSN-L : 1882-4889
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Velocity-Robust Gait Analysis for Human Identification through Constrained Learning of Stochastic Switched Auto-Regressive Model
Dapeng ZHANGShinkichi INAGAKITatsuya SUZUKI
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2013 Volume 6 Issue 5 Pages 331-340

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Abstract

Gait recognition is a promising non-intrusive biometric method. A robust and compact gait model is desirable in many security applications from public facilities to personal devices. Shape cues are chosen in most current researches except a few adopting dynamical features exclusively. And most of these systems are velocity-dependent. In order to explore more features of gait and to fit the varying environments of different applications, a new gait recognition model which synthesizes dynamic model and statistical one is designed. A kind of dynamical features, angular variables with respect to ankle joint, are adopted as the model's input. The proposed model has a circular structure consisted of 2 pairs of correlated states. A constrained learning algorithm is proposed under the model's special structure configured according to a 2-link virtual passive walking model which plays an important role both in the initialization and in the updating step. By evaluating the recognition rates of different models, the velocity-robust characteristics of the new model and its low computational load compared with conventional HMM are verified.

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© 2013 The Society of Instrument and Control Engineers
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