Abstract
AdaBoost algorithm is an efficient face detection method whose effectiveness is mainly influenced by the selection of weak classifier during the early process of training. To some extent, the selection of weak classifier depends on the selected sample set. Thus the training sample set is one of the most important factors in face detection. In this paper, the relationship between cascade classifier and weak classifier is analyzed in detail. Based on the factors of detection rate, undetected rate and false detection rate, an improved sample selection method is present and a fast face detection method which is divided into training and detection is proposed. The method is capable of optimizing the proportion of training samples and merging the detection window. The experimental results show that the proposed method is more effective than traditional ones.
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