EAGER: Spontaneous 4D-Facial Expression Corpus for Automated Facial Image Analysis

面部表情是人类经验的核心。它的高效和有效的测量是一个挑战，自动面部图像分析寻求解决。目前，很少有公开可用的带注释的数据库存在。那些可以做到的仅限于2D静态图像或摆姿势面部行为的视频。由于缺乏足够的训练数据，进一步的发展受到阻碍。由于摆姿势和不摆姿势（又名“自发”）的面部表情在包括复杂性在内的几个维度上有所不同，因此需要对不摆姿势的面部行为进行充分注释的视频。此外，由于人脸是一个三维可变形的物体，2D视频是不够的。需要3D视频存档。这个项目开发了一个3D视频语料库，记录了不同群体的年轻人自发的面部和声音表达。经过充分验证的情感归纳引出情感表达和副语言交流。序列级基础真值通过参与者自我报告获得。采用人脸动作编码系统对人脸动作单元进行编码，得到帧级真值。该项目促进了对细微面部表情的三维时空特征的探索，更好地理解面部动作单元中姿势与运动动力学的关系，以及对自然发生的面部动作的更深入的理解。该项目促进下一代情感计算在安全、执法、生物医学、行为科学、娱乐和教育领域的应用研究。多模态3D视频数据库及其元数据供研究团体用于新算法的开发、评估、比较和评估。

Facial expression is central to human experience. Its efficient and valid measurement is a challenge that automated facial image analysis seeks to address. Currently, few publically available, annotated databases exist. Those that do are limited to 2D static images or video of posed facial behavior. Further development is stymied by lack of adequate training data. Because posed and un-posed (aka ?spontaneous?) facial expressions differ along several dimensions including complexity, well annotated video of un-posed facial behavior is needed. Moreover, because the face is a three-dimensional deformable object, 2D video is insufficient. A 3D video archive is needed.This project develops a 3D video corpus of spontaneous facial and vocal expression in a diverse group of young adults. Well-validated emotion inductions elicit expressions of emotion and paralinguistic communication. Sequence-level ground truth is obtained via participant self-report. Frame-level ground-truth is obtained via facial action unit coding using the Facial Action Coding System. The project promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and motion dynamics in facial action units, and deeper understanding of naturally occurring facial action.The project promotes research on next-generation affective computing with applications in security, law-enforcement, biomedicine, behavior science, entertainment and education. The multimodal 3D video database and its metadata are for the research community for new algorithm development, assessment, comparison, and evaluation.