Face Attention Network: An Effective Face Detector for the Occluded Faces

TL;DR

This paper introduces Face Attention Network (FAN), a face detector that effectively improves recall for occluded faces like masks and sunglasses without sacrificing detection speed, achieving state-of-the-art results.

Contribution

The paper proposes a novel anchor-level attention mechanism combined with an anchor assign strategy and data augmentation, enhancing occluded face detection performance.

Findings

Achieved state-of-the-art results on WiderFace and MAFA benchmarks.

Significantly improved recall for occluded faces.

Maintained high detection speed despite improvements.

Abstract

The performance of face detection has been largely improved with the development of convolutional neural network. However, the occlusion issue due to mask and sunglasses, is still a challenging problem. The improvement on the recall of these occluded cases usually brings the risk of high false positives. In this paper, we present a novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed. More specifically, we propose a new anchor-level attention, which will highlight the features from the face region. Integrated with our anchor assign strategy and data augmentation techniques, we obtain state-of-art results on public face detection benchmarks like WiderFace and MAFA. The code will be released for reproduction.