A Fast Training Algorithm for Face detection
Zhong Xiang-yang · Journal of Jiaying University · 2007
Recently the human face detection system based on Adaboost is successfully used in application areas because of its high speed and accepted detection rates,but the Viola-Jones learning algorithm,in which the weak classifiers are retrained once for each feature in the cascade,is very slow.This paper presents a new methor to design node classifiers in the cascade detector.First,in our method training all weak classifiers are moved out of the loop per node,then we select the features that the ensemble classifier has the smallest error rate instead of choosing a single feature with the smallest weighted error.Experimental results prove that our training speed is faster than Viola-Jones′s methor.