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

面部表情是人类体验的核心。它的高效和有效的测量是自动面部图像分析寻求解决的挑战。目前，几乎没有公开可用的带注释的数据库。这些仅限于2D静态图像或摆姿势的面部行为视频。由于缺乏足够的培训数据，进一步的发展受到阻碍。因为摆姿势和不摆姿势(又名？自发？)面部表情在几个维度上都不同，包括复杂性，需要对未摆姿势的面部行为进行良好注释的视频。此外，由于人脸是三维可变形对象，2D视频是不够的。这个项目开发了一个在不同的年轻人群体中自发的面部和声音表达的3D视频语料库。经过充分验证的情感诱导可以引发情感表达和副语言交流。通过参与者自我报告获得序列级别的地面真实信息。通过使用面部动作编码系统的面部动作单元编码来获得帧级别的地面真实。该项目促进了对微妙面部表情的3D时空特征的探索，更好地理解了面部动作单元中姿势和运动动力学之间的关系，以及对自然发生的面部动作的更深入的理解。该项目促进了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用于研究社区新算法的开发、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.