WFLW

Wider Facial Landmarks in-the-wild (WFLW) contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks. Apart from landmark annotation, out new dataset includes rich attribute annotations, i.e., occlusion, pose, make-up, illumination, blur and expression for comprehensive analysis of existing algorithms. Compare to previous dataset, faces in the proposed dataset introduce large variations in expression, pose and occlusion. We can simply evaluate the robustness of pose, occlusion, and expression on proposed dataset instead of switching between multiple evaluation protocols in different datasets.

野外人脸关键点（Wider Facial Landmarks in-the-wild，WFLW）数据集包含10000张人脸图像，其中7500张用于训练集，2500张用于测试集，所有图像均配有98个经人工手动标注的人脸关键点。除人脸关键点标注外，本数据集还包含丰富的属性标注信息，具体包括遮挡、姿态、妆容、光照、模糊程度与表情，可用于对现有算法开展全面分析。相较于此前发布的各类数据集，本数据集收录的人脸在表情、姿态与遮挡场景上存在较大差异。研究人员可直接在本数据集上完成针对姿态、遮挡及表情鲁棒性的评估，无需在不同数据集间切换多种评估协议。

搜集汇总

背景与挑战

背景概述

WFLW是一个包含10,000张人脸图像的面部关键点标注数据集，每张图像有98个手动标注的关键点，并提供了丰富的属性标注如遮挡、姿态和表情等，用于评估算法在不同条件下的鲁棒性。