Proceedings of 2012 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
巻, 号, ページ
出版年月
2012年12月
出版者
和文:
英文:
会議名称
和文:
英文:
2012 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
開催地
和文:
ハリウッド
英文:
Los Angeles
アブストラクト
In gait-based person identification, statistical methods such as hidden Markov models (HMMs) have been proved to be effective. Their
performance often degrades, however, when the amount of training data for each walker is insufficient. In this paper, we propose walker
adaptation and walker adaptive training, where the data from the other walkers are effectively utilized in the model training. In walker
adaptation, maximum likelihood linear regression (MLLR) is used to transform the parameters of the walker-independent model to those of
the target walker model. In walker adaptive training, we effectively exclude the inter-walker variability from the walker-independent model.
In our evaluation, our methods improved the identification performance even when the amount of data was extremely small.
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