超越FACS：数据驱动的面部表情字典及其在预测自闭症中的应用
Beyond FACS: Data-driven Facial Expression Dictionaries, with Application to Predicting Autism
AI总结：
针对FACS编码成本高且AU检测准确率不足的问题，提出无监督的数据驱动编码系统Facial Basis，通过可加的局部3D面部运动单元全面重建可观测运动，在预测自闭症诊断上优于常用AU检测器。
摘要：
面部动作编码系统（FACS）已被众多研究用于探究面部行为与心理健康之间的联系。FACS编码过程费力且昂贵，这促使了用于动作单元（AU）检测的机器学习框架的发展。尽管历经三十年的不懈努力，许多AU的检测准确率仍被认为低于行为研究所需的阈值。此外，许多AU被完全排除在外，使得实现FACS的最终目标——完整表示任何面部表情——变得不可能。本文考虑了一种替代方法。我们不再创建模仿FACS专家的自动化工具，而是提出使用一种模仿FACS关键特性的新编码系统。具体而言，我们构建了一种数据驱动的编码系统，称为Facial Basis，它包含对应于局部且可解释的3D面部运动的单元，并克服了自动化FACS编码的三个结构性局限。首先，该方法完全无监督，避开了昂贵、费力且存在变异性的手动标注。其次，Facial Basis能重建所有可观测运动，而非依赖有限的、可识别运动库（如自动化FACS那样）。最后，Facial Basis单元是可加的，而AU在以非可加组合出现时可能无法被检测。在从面对面和远程对话中预测自闭症诊断方面，所提方法优于最常用的AU检测器，凸显了全面编码面部行为的重要性。据我们所知，Facial Basis是首个用于将视频中面部表情解构为局部运动的FACS替代方案。我们在github.com/sariyanidi/FacialBasis提供了该方法的开源实现。
英文摘要：
The Facial Action Coding System (FACS) has been used by numerous studies to investigate the links between facial behavior and mental health. The laborious and costly process of FACS coding has motivated the development of machine learning frameworks for Action Unit (AU) detection. Despite intense efforts spanning three decades, the detection accuracy for many AUs is considered to be below the threshold needed for behavioral research. Also, many AUs are excluded altogether, making it impossible to fulfill the ultimate goal of FACS-the representation of any facial expression in its entirety. This paper considers an alternative approach. Instead of creating automated tools that mimic FACS experts, we propose to use a new coding system that mimics the key properties of FACS. Specifically, we construct a data-driven coding system called the Facial Basis, which contains units that correspond to localized and interpretable 3D facial movements, and overcomes three structural limitations of automated FACS coding. First, the proposed method is completely unsupervised, bypassing costly, laborious and variable manual annotation. Second, Facial Basis reconstructs all observable movement, rather than relying on a limited repertoire of recognizable movements (as in automated FACS). Finally, the Facial Basis units are additive, whereas AUs may fail detection when they appear in a non-additive combination. The proposed method outperforms the most frequently used AU detector in predicting autism diagnosis from in-person and remote conversations, highlighting the importance of encoding facial behavior comprehensively. To our knowledge, Facial Basis is the first alternative to FACS for deconstructing facial expressions in videos into localized movements. We provide an open source implementation of the method at github.com/sariyanidi/FacialBasis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
