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Layered time series model for gait recognition

Layered time series model for gait recognition

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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.

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