论文标题
锚节：大姿势的基于锚的面部标志性检测器
AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses
论文作者
论文摘要
面部地标的本地化旨在检测人脸的预定点，并且随着基于神经网络的方法的最新发展，该主题得到了迅速改善。但是，在不受约束的情况下，尤其是姿势变化很大，这仍然是一项具有挑战性的任务。在本文中，我们针对大型姿势面部地标本地化的问题，并基于分裂和聚集的策略来解决此任务。为了拆分搜索空间，我们建议一组锚模板作为回归的参考，很好地解决了面部姿势的较大变化。根据每个锚模板的预测，我们建议将结果汇总，这可以减少由于大姿势而导致的地标不确定度。总体而言，我们提出的方法称为Anchorface，在四个具有挑战性的基准（即AFLW，300W，Menpo和WFLW数据集）上以极有效的推理速度获得了最先进的结果。代码将在https://github.com/nothingelse92/anchorface上找到。
Facial landmark localization aims to detect the predefined points of human faces, and the topic has been rapidly improved with the recent development of neural network based methods. However, it remains a challenging task when dealing with faces in unconstrained scenarios, especially with large pose variations. In this paper, we target the problem of facial landmark localization across large poses and address this task based on a split-and-aggregate strategy. To split the search space, we propose a set of anchor templates as references for regression, which well addresses the large variations of face poses. Based on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses. Overall, our proposed approach, named AnchorFace, obtains state-of-the-art results with extremely efficient inference speed on four challenging benchmarks, i.e. AFLW, 300W, Menpo, and WFLW dataset. Code will be available at https://github.com/nothingelse92/AnchorFace.
