COMPUTER VISION ANALYSIS OF DYNAMIC FACIAL BEHAVIOR

描述（申请人摘要）：面部表情为研究情绪、认知过程和社会互动提供了重要的行为测量，并包含神经系统疾病和抑郁症的诊断信息。与脑电图、EMO、ANS或脑成像测量相比，视频中的面部表情测量作为情绪活动的指标侵入性更小。目前由人类专家进行测量。本研究的目标是利用计算机视觉技术开发一个自动识别、测量和编码视频面部表情的系统。该项目将利用动力系统的概率模型来修改观察到的图像序列背后的面部行为。这些技术包括隐马尔可夫模型，以及由Movellan及其同事开发的一种新的随机建模技术，称为扩散网络[38]。与传统的动态模型相比，扩散网络具有允许连续时间动态和连续状态的优势。一个自动化的系统将使面部表情测量作为一种研究工具在行为科学和情绪的神经基础调查中更广泛地使用。

DESCRIPTION (Applicant's abstract): Facial expressions provide an important behavioral measure for the study of emotion, cognitive processes, and social interaction, and contain diagnostic information for neurological disorders and depression. Facial expression measurement from video is less intrusive than EEG, EMO, ANS or brain imaging measurements as an indicator of emotional activity. The measurement is presently performed by human experts. The goal of this research is to develop an automatic system for recognition, measurement, and coding of facial expressions from video using computer vision technology. This project will utilize probabilistic models of dynamical systems to mod& the facial behavior underlying the observed image sequences. These techniques include hidden Markov models, and a new stochastic modeling technique developed by Movellan and colleagues called diffusion networks [38]. Diffusion networks offer the advantage over traditional dynamical models of allowing continuous time dynamics and continuous states. An automated system would make facial expression measurement more widely accessible as a research tool in behavioral science and investigations of the neural substrates of emotion.