Enhancing Interior and Exterior Deep Facial Features for Face Detection in the Wild

Chenchen Zhu, Yutong Zheng, Khoa Luu, Marios Savvides

Abstract: Although face detection has been intensely studied for decades, it is still a challenging topic due to numerous conditions, e.g. heavy occlusions, low resolutions, extreme poses, non-face patterns that look like human faces, etc. This paper proposes a novel region-based ConvNet to address these issues. Our approach enhances the interior deep facial features and explicitly incorporates the exterior deep features. The enhanced interior features provide fine details for small faces. The exterior features capture the local information surrounding the face, supporting the detection under challenging conditions. Experiments show that our proposed components improve the baseline method significantly. Additionally, our approach consistently achieves competitive performance in four challenging databases, i.e. Wider Face, AFW, PASCAL Faces, and FDDB. We also introduce a new challenging non-face dataset 1 of 6,000 images to benchmark false positive rates for future research.