Fast Feature Value Searching for Face Detection

Yunyang Yan, Zhibo Guo, Jingyu Yang

Abstract


It would cost much and much time in face detector training using AdaBoost algorithm. An improved face detection algorithm called Rank-AdaBoost based on feature-value-division and Dual-AdaBoost based on dual-threshold are proposed to accelerate the training and improve detection performance. Using the improved AdaBoost, the feature values with respect to each Haar-like feature are rearrange to a definite number of ranks.The number of ranks is much less than that of the training samples, so that the test time on each training samples is saved corresponding to the original AdaBoost algorithm. Inheriting cascaded frame is also proposed here. Experimental results on MIT-CBCL face & nonface training data set illustrate that the improved algorithm could make training process convergence quickly and the training time is only one of 50 like before. Experimental results on MIT+CMU face set also show that the detection speed and accuracy are both better than the original method.


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Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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