Particle swarm optimization based AdaBoost for face detection

Authors:
Ammar W. Mohemmed;Mengjie Zhang;Mark John'ston
Affiliations:
School of Engineering and Computer Science and Victoria University of Wellington, Wellington, New Zealand;School of Engineering and Computer Science and Victoria University of Wellington, Wellington, New Zealand;School of Mathematics, Statistics and Operations Research and Victoria University of Wellington, Wellington, New Zealand
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

This paper proposes a PSOAdaBoost algorithm incorporating Particle Swarm Optimization within an AdaBoost framework for face detection applications. The basic component of an AdaBoost detector is a weak classifier, consisting of a feature, selected by an exhaustive search mechanism, and a decision threshold. The proposed PSOAdaBoost computes the best feature and optimizes the threshold in one optimization process. Experiments between the proposed algorithm and AdaBoost (with exhaustive feature selection) suggest that PSOAdaBoost has better performance in terms of much less training time and better classification accuracy.