Real-time rotation invariant face detection based on cost-sensitive AdaBoost

This paper presents a novel method of detecting faces at any degree of rotation in the image plane based on cost-sensitive AdaBoost (CS-AdaBoost) algorithm. The method first employs a cascade of very simple classifiers trained by CS-AdaBoost to determine the possible orientation of each input window and then uses an upright face detector also trained by CS-AdaBoost to verify the derotated face candidate. The training procedures for both classifiers are also given in the paper. Experimental results show that the proposed method gives higher detection ratio and real-time detection speed compared to the conventional ones.