Automated Facial Expression Analysis for Research and Clinical Use

描述(申请人提供)：一百多年来，面部表情一直是情绪研究的焦点。近几十年来，对面部表情的观察对精神病理学的病因学产生了批判性和戏剧性的见解，并被证明能够预测治疗结果(见Ekman&Rosenberg，2005)。尽管有这些最初令人震惊的发现，但令人惊讶的是，后续工作很少。缺乏持续研究的主要原因是，最可靠的测量面部表情的手动系统通常需要大量培训，而且是劳动密集型的。使用计算机视觉和机器学习的自动测量寻求解决对有效、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，使用姿势行为或结构化背景与同伙，或训练有素的采访者，或预先训练(特定于人的)脸部模型。为了将自动编码应用于真实世界的设置，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分可变性的大型数据库。我们开发了一个独特的数据库，以满足这一需求和实现健壮的自动编码所需的算法。该数据库由720名参与者组成，他们分成三个小组，参与一个小组组建任务。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限制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对面部表情的高效、准确和有效的测量，我们的目标是1)训练和验证分类器，以在这个前所未有的海量、多样化的数据集上实现可靠的面部表情检测；2)将以前针对特定人的方法扩展到独立于人的(通用)面部特征检测、跟踪和对齐；以及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.