MaskFace: multi-task face and landmark detector
This work addresses the need for efficient and accurate facial analysis tools, though it is incremental as it builds on existing face detection methods by adding a keypoint prediction head.
The paper tackles the problem of facial analysis by proposing a multi-task model that simultaneously performs face detection and landmark localization, achieving state-of-the-art results on multiple datasets.
Currently in the domain of facial analysis single task approaches for face detection and landmark localization dominate. In this paper we draw attention to multi-task models solving both tasks simultaneously. We present a highly accurate model for face and landmark detection. The method, called MaskFace, extends previous face detection approaches by adding a keypoint prediction head. The new keypoint head adopts ideas of Mask R-CNN by extracting facial features with a RoIAlign layer. The keypoint head adds small computational overhead in the case of few faces in the image while improving the accuracy dramatically. We evaluate MaskFace's performance on a face detection task on the AFW, PASCAL face, FDDB, WIDER FACE datasets and a landmark localization task on the AFLW, 300W datasets. For both tasks MaskFace achieves state-of-the-art results outperforming many of single-task and multi-task models.