Automated Facial Expression Analysis for Research and Clinical Use

描述（由申请人提供）：一百多年来，面部表情一直是情感研究的焦点。近几十年来，对面部表情的观察已经产生了关于精神病理学病因的关键和戏剧性的见解，并已被证明能够预测治疗结果（见Ekman和Rosenberg， 2005）。尽管有这些最初的惊人发现，但令人惊讶的是，很少有后续研究。缺乏持续研究的主要原因是，用于测量面部表情的最可靠的人工系统通常需要大量的训练，并且是劳动密集型的。使用计算机视觉和机器学习的自动测量旨在解决对有效、高效和可重复测量的需求。最近的系统在相当小的研究中显示出了希望，这些研究使用了与同伙、训练有素的采访者或预先训练的（个人特定的）面部模型的姿势行为或结构化上下文。为了在现实环境中应用自动编码，需要一个在姿势、头部运动、肤色、性别、部分遮挡和表情强度方面具有充足可变性的大型数据库。我们已经开发了一个独特的数据库，以满足这一需求和必要的算法，使强大的自动编码。该数据库由720名参与者组成，他们以三人为一组，进行小组组建任务。在一项初步研究中，我们证明了我们的算法可以在这种相对不受约束的环境中成功地编码与人类情感相关的两个关键面部信号（Cohn & Sayette, 2010）。为了在研究和临床环境中实现高效、准确和有效的面部表情测量，我们的目标是：1)训练和验证分类器，以便在这个前所未有的庞大、多样化的数据集上实现可靠的面部表情检测；2)将之前针对个人的方法扩展到独立于个人（通用）的面部特征检测、跟踪和对齐；3)使这些工具可用于研究和临床使用。

DESCRIPTION (provided by applicant): Facial expression has been a focus of emotion research for over a hundred years. In recent decades observations of facial expressions have yielded critical and dramatic insights about the etiology of psychopathology, and have proven capable of predicting treatment outcomes (see Ekman & Rosenberg, 2005). Despite these original striking findings, there has been surprisingly little follow-up work. The primary reason fr the lack of sustained research is that the most reliable manual systems for measuring facial expression often require considerable training and are labor intensive. Automated measurement using computer vision and machine learning seeks to address the need for valid, efficient, and reproducible measurement. Recent systems have shown promise in fairly small studies using posed behavior or structured contexts with confederates, or trained interviewers, or pre-trained (person-specific) face models. For automated coding to be applied in real-world settings, a large data base with ample variability in pose, head motion, skin color, gender, partial occlusion, and expression intensity is needed. We have developed a unique database that meets this need and the algorithms necessary to enable robust automated coding. The database consists of 720 participants in three-person groups engaged in a group formation task. In a preliminary study, we demonstrated that our algorithms can successfully code two key facial signals associated with human emotion in this relatively unconstrained context (Cohn & Sayette, 2010). To achieve efficient, accurate, and valid measurement of facial expression usable in research and clinical settings, we aim to 1) train and validate classifiers to achieve reliable facial expression detectin across this unprecedentedly large, diverse data set; 2) extend the previous person-specific methods to person-independent (generic) facial feature detection, tracking, and alignment; and 3) make these tools available for research and clinical use.