Abstract
Facial landmark localization is a very crucial step in numerous face related applications, such as face recognition, facial pose estimation, face image synthesis, etc. However, previous competitions on facial landmark localization (i.e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components. In order to overcome this problem, we construct a challenging dataset, named JD-landmark. Each image is manually annotated with 106-point landmarks. This dataset covers large variations on pose and expression, which brings a lot of difficulties to predict accurate landmarks. We hold a 106-point facial landmark localization competition1 on this dataset in conjunction with IEEE International Conference on Multimedia and Expo (ICME) 2019. The purpose of this competition is to discover effective and robust facial landmark localization approaches.
Abstract (translated)
人脸地标定位是人脸识别、人脸姿态估计、人脸图像合成等众多人脸相关应用中非常关键的一步，但以往的人脸地标定位竞赛（即300-W、300-VW和Menpo挑战赛）旨在预测68个无法描述人脸地标的点。面部组件的结构。为了克服这个问题，我们构建了一个具有挑战性的数据集，命名为JD Landmark。每幅图像都用106个点标志手动标注。这个数据集涵盖了姿势和表情上的巨大变化，这给准确预测地标带来了许多困难。我们与IEEE国际多媒体与博览会（ICME）2019联合举办了一场关于该数据集的106点面部地标定位竞赛1。本次比赛的目的是发现有效和强大的面部标志性定位方法。
URL
https://arxiv.org/abs/1905.03469
