Occlusion Robust Face Detection Using Shallow and Deep Proposal Based Faster R CNN
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 real world 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 coarse to 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.
CCBR