A sequential Monte Carlo method for Bayesian face recognition

被引：0

作者：

Matsui, Atsushi

Clippingdale, Simon

Matsumoto, Takashi

机构：

[1] NHK Japan Broadcasting Corp, Japan Broadcasting Corp, Sci & Tech Res Lab, Setagaya Ku, Tokyo 1578510, Japan

[2] Waseda Univ, Dept Elect Engn & Biosci, Shinjuku Ku, Tokyo 1698555, Japan

来源：

STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, PROCEEDINGS | 2006年 / 4109卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a Sequential Monte Carlo (SMC) learning algorithm for Bayesian probability distributions that describe model parameters in a video face recognition system based on deformable template matching. The new algorithm achieves significantly improved robustness of recognition against facial expressions and speech movements by comparison with a baseline batch MCMC (Markov Chain Monte Carlo) algorithm, at no additional computational cost. Experimental results demonstrate the effectiveness and computational efficiency of the new algorithm.

引用

页码：578 / 586

页数：9

共 10 条

[1] An introduction to MCMC for machine learning [J].

Andrieu, C ;

de Freitas, N ;

Doucet, A ;

Jordan, MI .

MACHINE LEARNING, 2003, 50 (1-2) :5-43

[2]

Clippingdale S., 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), P662, DOI 10.1109/ICIP.1999.821718

[3]

CLIPPINGDALE S, 2000, P MVA2000 TOK JAP, P5

[4]

Doucet A., 1998, CUEDFINFENGTR310

[5]

LIU JS, 2001, MONTE CARLO STRATEGI, P53

[6]

MacKay D, 2003, Information Theory, Inference, and Learning Algorithms

[7]

Mardia K., 2009, DIRECTIONAL STAT

[8]

MATSUI A, 2004, P ICPR2004, V3, P918

[9]

Matsumoto T, 2005, AIP CONF PROC, V803, P129, DOI 10.1063/1.2149788

[10]

WISKOTT L, 1996, TR9608 RUHR U I NEUR

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