  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.
