Gait Recognition using Cyclic HMMs(Gestures)

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概要

• 論文の詳細を見る

• Recently human gait has been considered as a useful biometric supporting high performance human identification systems. We propose a view-based pedestrian identification method using the dynamic silhouettes of a human body modeled with the hidden Markov model (HMM). Two types of gait models have been developed both with a cyclic architecture: one is a discrete HMM method using a self-organizing map-based VQ codebook and the other is a continuous HMM method using feature vectors transformed into a PCA space. Experimental results showed a consistent performance trend over a range of model's parameters and the recognition rate up to 88.1%. Compared with other methods, the proposed models and techniques are believed to have a sufficient potential for a successful application to gait recognition.

• 社団法人電子情報通信学会の論文
• 2006-11-17

著者

• Sin Bong-kee

  Computer Engineering Pukyong National University

• Suk Heung-II

  Computer Engineering, Pukyong National University

• Suk Heung-ii

  Computer Engineering Pukyong National University

• Suk Heung-Il

  Computer Engineering, Pukyong National University

関連論文

• Gait Recognition using Cyclic HMMs(Gestures)

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