Pruned Resampling : Probabilistic Model Selection Schemes for Sequential Face Recognition(<Special Section>Image Recognition and Understanding)
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概要
- 論文の詳細を見る
This paper proposes probabilistic pruning techniques for a Bayesian video face recognition system. The system selects the most probable face model using model posterior distributions, which can be calculated using a Sequential Monte Carlo (SMC) method. A combination of two new pruning schemes at the resampling stage significantly boosts computational efficiency by comparison with the original online learning algorithm. Experimental results demonstrate that this approach achieves better performance in terms of both processing time and ID error rate than a contrasting approach with a temporal decay scheme.
- 社団法人電子情報通信学会の論文
- 2007-08-01
著者
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Matsui Atsushi
Science And Technical Research Laboratories Nhk (japan Broadcasting Corporation):faculty Of Science
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Matsui Atsushi
Science And Technical Research Laboratories
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Matsumoto Takashi
Faculty Of Engineering Department Of Electronic Engineering Yamanashi University
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CLIPPINGDALE Simon
Science and Technical Research Laboratories, NHK (Japan Broadcasting Corporation)
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Clippingdale Simon
Science And Technical Research Laboratories Nhk (japan Broadcasting Corporation)
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Matsumoto Takashi
Faculty Of Science And Engineering Waseda University
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Matsumoto Takashi
Faculty Of Agriculture Shinshu University
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- Pruned Resampling : Probabilistic Model Selection Schemes for Sequential Face Recognition(Image Recognition and Understanding)
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