Face Detection Based on Multiple Thresholds Boosting Method
Jie Ling · Jisuanji gongcheng · 2009
Aiming at the problem that the Adaboost algorithm using simple threshold weak classifiers is too weak to fit complex distributions and its slow convergence rate in training,this paper designs a multiple thresholds weak learner.This learner splits the nodes by using biggest reduction in the sum of squares as the partition criteria and builds a weak classifier.It boosts weak classifiers using GAB algorithm on training dataset.Experimental results show that the false positive rate of this method is lower than Adaboost algorithm at the same number of weak classifiers.