Use of Active Appearance Models for analysis and synthesis of naturally occurring behavior

J. Cohn
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引用次数: 1

Abstract

Significant efforts have been made in the analysis and understanding of naturally occurring behavior. Active Appearance Models (AAM) are an especially exciting approach to this task for facial behavior. They may be used both to measure naturally occurring behavior and to synthesize photo-realistic real-time avatars with which to test hypotheses made possible by those measurements. We have used both of these capabilities, analysis and synthesis, to investigate the influence of depression on face-to-face interaction. With AAMs we have investigated large datasets of clinical interviews and successfully modeled and perturbed communicative behavior in a video conference paradigm to test causal hypotheses. These advances have lead to new understanding of the social functions of depression and dampened affect in dyadic interaction. Key challenges remain. These include automated detection and synthesis of subtle facial actions; hybrid methods that optimally integrate automated and manual processing; computational modeling of subjective states from multimodal input; and dynamic models of social and affective behavior.
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使用活动外观模型分析和合成自然发生的行为
在分析和理解自然发生的行为方面已经做出了重大努力。主动外观模型(AAM)是研究面部行为的一种特别令人兴奋的方法。它们既可以用来测量自然发生的行为,也可以用来合成逼真的实时化身,用来测试由这些测量产生的假设。我们使用了这两种能力,分析和综合,来调查抑郁症对面对面互动的影响。通过AAMs,我们调查了大量临床访谈数据集,并成功地在视频会议范式中模拟和干扰了沟通行为,以检验因果假设。这些进展使人们对抑郁症的社会功能和二元互动中的抑制情绪有了新的认识。主要挑战依然存在。其中包括自动检测和合成微妙的面部动作;混合方法,最佳地集成自动化和人工处理;基于多模态输入的主观状态计算建模以及社会和情感行为的动态模型。
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