Journal
PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE, VOL 1: ECTA
Volume -, Issue -, Pages 156-163Publisher
SCITEPRESS
DOI: 10.5220/0006041101560163
Keywords
Object Detection; Genetic Algorithms; Haar Features; Adaboost; Face Detection
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Boosted cascade of simple features, by Viola and Jones, is one of the most famous object detection frameworks. However, it suffers from a lengthy training process. This is due to the vast features space and the exhaustive search nature of Adaboost. In this paper we propose GAdaboost: a Genetic Algorithm to accelerate the training procedure through natural feature selection. Specifically, we propose to limit Adaboost search within a subset of the huge feature space, while evolving this subset following a Genetic Algorithm. Experiments demonstrate that our proposed GAdaboost is up to 3.7 times faster than Adaboost. We also demonstrate that the price of this speedup is a mere decrease (3%, 4%) in detection accuracy when tested on FDDB benchmark face detection set, and Caltech Web Faces respectively.
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