Subject Adaptation and Adaptive Training for Gait-based Person Identification
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概要
- 論文の詳細を見る
The performance of gait-based person identification using statistical methods such as hidden Markov models (HMMs) often degrades when the amount of training data for each subject is insufficient. In our previous study, we proposed a gait-based person identification method using HMMs where we trained subject-dependent model from the scratch using each subject's training data. In this paper, we propose a model adaptation scheme, where the data from the other subjects are effectively utilized in the model training. We further improve the adaptation performance using subject adaptive training by effectively excluding the inter-subject variability from the subject-independent model. The proposed method improved the identification performance even when the amount of data was extremely small.
- 2012-02-02
著者
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Rasyid Aqmar
東京工業大学 情報理工学研究科 計算工学専攻
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Shinoda Koichi
Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut
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Rasyid Aqmar
Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut
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Furui Sadaoki
Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut
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Aqmar Rasyid
Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institute Of Technology.
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