论文标题
maskface：多任务脸和地标探测器
MaskFace: multi-task face and landmark detector
论文作者
论文摘要
目前，面部分析的领域单个任务方法的面部检测和地标定位占主导地位。在本文中，我们提请注意多任务模型同时解决这两个任务。我们提出了一个高度准确的面部和地标检测模型。该方法称为MaskFace，通过添加关键点预测头扩展了先前的面部检测方法。新的Kepoint Head通过用Roialign层提取面部特征来采用面膜R-CNN的想法。在图像中几乎没有面孔的情况下，键盘头会增加小型计算开销，同时显着提高准确性。我们在AFW，Pascal Face，FDDB，较宽的面部数据集和AFLW（300W数据集）上的Landmark本地化任务上评估了Maskface在面部检测任务上的性能。对于这两个任务，MaskFace都能达到最新的结果，其结果优于许多单任务和多任务模型。
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.
