Occlusion-robust face detection using shallow and deep proposal based faster R-CNN

Abstract

As the first essential step of automatic face analysis, face detection always receives high attention. The performance of current state-of-the-art face detectors cannot fulfill the requirements in realworld scenarios especially in the presence of severe occlusions. This paper proposes a novel and effective approach to occlusion-robust face detection. It combines two major phases, i.e. proposal generation and classification. In the former, we combine both the proposals given by a coarseto- fine shallow pipeline and a Region Proposal Network (RPN) based deep one respectively, to generate a more comprehensive set of candidate regions. In the latter, we further decide whether the regions are faces using a well-trained Faster R-CNN. Experiments are conducted on the WIDER FACE benchmark, and the results clearly prove the competency of the proposed method at detecting occluded faces.