  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.
