Context-Anchors for Hybrid Resolution Face Detection
Tianpeng Wu, Dong Liang, Jiaxing Pan, Shun’ichi Kaneko · 2019
Despite the positive trends in the development of face detection, open challenges still exist, such as the detection of degraded faces caused by small-size, defocus blur and occlusion in surveillance video. When utilizing anchor-based methods, the anchors are the basic units of training samples, and their ranges are proportional to the ranges of the original label boxes (ground truth). This paper argues that the selected range of an anchor is crucial for a detection task and proposes a face detection model CAHR (context-anchors for hybrid-resolution model) to balance the image resolution and the spatial context range for the purposes of locating small faces. In the training phase, specific size of spatial context is introduced for each anchor, and an image pyramid is employed for a dual CNNs model. In experiments, the indepth analysis of amplification ratio of the anchor and the detection rate is revealed. The detection rate of the small faces is improved by using the proposed model. It is also validated with a massively face datasets (WIDER FACE), demonstrating its superiority to the original hybrid-resolution model (HR) and some other advanced methods.