EmotioNet 面部表情数据集

The Emotionet database includes 950,000 images with annotated AUs. These were annotated with the algorithm described in Benitez-Quiroz, C. F., Srinivasan, R., & Martinez, A. M. (2016). EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR'16), Las Vegas, NV, USA. You can train your system using this set. You can also use any other annotated dataset you think appropriate. This dataset has been used to successfully train a variety of classifiers, including several deep networks.We also include 25K (24,600 to be precise) manually annotated AUs. You may want to use this dataset to see how well your algorithm works or to optimize the parameters of your algorithm.

EmotioNet数据库（EmotioNet）包含95万张带有标注动作单元（Action Units，简称AUs）的图像。这些图像采用Benitez-Quiroz、Srinivasan与Martinez于2016年提出的算法完成标注，对应论文为《EmotioNet：面向野外百万级面部表情自动标注的精准实时算法》，发表于2016年IEEE国际计算机视觉与模式识别会议（CVPR'16），举办地为美国内华达州拉斯维加斯。研究人员可依托该数据集训练自身系统，亦可选用任意符合需求的已标注数据集。该数据集已被成功应用于多种分类器的训练，其中涵盖多款深度神经网络。本数据集还附带2.5万张（准确为24600张）人工标注的AUs数据。您可借助该数据集测试自身算法的性能表现，或是优化算法的参数设置。

搜集汇总

背景与挑战

背景概述

EmotioNet是一个大规模面部表情数据集，包含95万张算法标注和2.46万张人工标注的面部动作单元(AU)图像，适用于训练表情识别分类器和算法优化。该数据集采用CVPR'16发表的先进标注算法，已被成功应用于多种机器学习模型的训练。