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Currently in the domain of facial analysis single task approaches for facedetection and landmark localization dominate. In this paper we draw attentionto multi-task models solving both tasks simultaneously. We present a highlyaccurate model for face and landmark detection. The method, called MaskFace,extends previous face detection approaches by adding a keypoint predictionhead. The new keypoint head adopts ideas of Mask R-CNN by extracting facialfeatures with a RoIAlign layer. The keypoint head adds small computationaloverhead in the case of few faces in the image while improving the accuracydramatically. We evaluate MaskFace's performance on a face detection task onthe AFW, PASCAL face, FDDB, WIDER FACE datasets and a landmark localizationtask on the AFLW, 300W datasets. For both tasks MaskFace achievesstate-of-the-art results outperforming many of single-task and multi-taskmodels.

MaskFace: multi-task face and landmark detector