Abstract
This paper introduces a novel anchor design principle
to support anchor-based face detection for superior scaleinvariant performance, especially on tiny faces. To achieve
this, we explicitly address the problem that anchor-based
detectors drop performance drastically on faces with tiny
sizes, e.g. less than 16 × 16 pixels. In this paper, we investigate why this is the case. We discover that current anchor design cannot guarantee high overlaps between tiny
faces and anchor boxes, which increases the difficulty of
training. The new Expected Max Overlapping (EMO) score
is proposed which can theoretically explain the low overlapping issue and inspire several effective strategies of new
anchor design leading to higher face overlaps, including
anchor stride reduction with new network architectures, extra shifted anchors, and stochastic face shifting. Comprehensive experiments show that our proposed method significantly outperforms the baseline anchor-based detector,
while consistently achieving state-of-the-art results on challenging face detection datasets with competitive runtime
speed