Pruned Resampling: Probabilistic Model Selection Schemes for Sequential Face Recognition

Authors:
Atsushi Matsui;Simon Clippingdale;Takashi Matsumoto
Affiliations:
-;-;-
Venue:
IEICE - Transactions on Information and Systems
Year:
2007

Citing 3
Cited 0

Information Theory, Inference & Learning Algorithms

Information Theory, Inference & Learning Algorithms
Bayesian Face Recognition using a Markov Chain Monte Carlo Method

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A sequential monte carlo method for bayesian face recognition

SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.