Pruned Resampling : Probabilistic Model Selection Schemes for Sequential Face Recognition
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- MATSUI Atsushi
- 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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Description
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.
Journal
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- IEICE Trans. on Information and Systems, D
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IEICE Trans. on Information and Systems, D 90 (8), 1151-1159, 2007-08-01
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1570291227637861632
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- NII Article ID
- 110007538639
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- NII Book ID
- AA10826272
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- ISSN
- 09168532
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- Text Lang
- en
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- Data Source
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- CiNii Articles
