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

面部表情是人类经验的核心。其有效和有效的测量是自动面部图像分析试图解决的挑战。目前，几乎没有可用的注释数据库。那些只限于2D静态图像或面部行为的视频。由于缺乏足够的培训数据，进一步的发展受到阻碍。因为posed和un-posed（aka？自发的？）面部表情在包括复杂度在内的多个维度上沿着不同，因此需要非姿态面部行为的注释良好的视频。 此外，因为面部是三维可变形对象，所以2D视频是不够的。本项目开发了一个3D视频语料库，记录了不同年龄组的年轻人自发的面部表情和声音表达。有效的情感诱导可以引发情感表达和非语言交际。序列水平的基础事实是通过参与者自我报告获得的。使用面部动作编码系统通过面部动作单元编码获得帧级地面实况。该项目将促进对面部细微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和运动动力学之间的关系，以及更深入地理解自然发生的面部动作。该项目将促进下一代情感计算的研究，并将其应用于安全、执法、生物医学、行为科学、娱乐和教育。多模式3D视频数据库及其元数据供研究社区用于新算法的开发、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.