COMPUTER VISION ANALYSIS OF DYNAMIC FACIAL BEHAVIOR

描述（申请人摘要）：面部表情为情绪、认知过程和社会互动的研究提供了重要的行为测量，并且包含神经障碍和抑郁症的诊断信息。作为情绪活动的指标，来自视频的面部表情测量比EEG、EMO、ANS或大脑成像测量的侵入性更小。目前，测量由人类专家进行。本研究的目的是利用计算机视觉技术，开发一个从视频中识别、测量和编码面部表情的自动系统。这个项目将利用动力系统的概率模型来模拟观察到的图像序列的面部行为。这些技术包括隐马尔可夫模型，以及Movellan及其同事开发的称为扩散网络的新随机建模技术[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点。一个自动化的系统将使面部表情测量更广泛地作为行为科学和情感神经基础研究的一种研究工具。

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.