Flipping and blending based highly robust in-plane and out-of-plane color face detection

Abstract

Face detection is very important for video surveillance, human-computer interaction, and face recognition. In this paper, a very robust face detection algorithm that can well detect rotated, in-plane, and out-of-plane faces without large amount of training data is proposed. First, several techniques, including the entropy rate superpixel (ERS) and the skin filter, are applied to obtain face candidate regions. Then, angle compensation and non-maximum suppression are applied to improve the accuracy of face detection. Moreover, to find out-of-plane faces, one can apply the flipping-and-blending technique, i.e., blending the face candidate with its flipping version to create a face that is similar to the frontal one. With it, even if there are no training data for out-of-plane faces, one can successfully detect the faces in the out-of-plane case. Simulations on the FEI dataset and the BaoFace dataset show that the proposed algorithm is efficient and outperforms state-of-the-art face detection approaches.