IEEE Transactions on Pattern Analysis and Machine Intelligence Coding , Analysis , Interpretation , and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence Coding , Analysis , Interpretation , and Recognition of Facial Expressions
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发表时间：
2007
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通讯作者：
Irfan Essa;A. Pentland
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作者：
Irfan Essa;A. Pentland
我们描述了一个计算机视觉系统，用于观察面部运动，通过使用一个最佳的估计光流方法加上一个几何和物理（肌肉）模型描述的面部结构。我们的方法产生一个可靠的参数表示的脸的独立肌肉动作组，以及面部运动的准确估计。以前的面部表情分析工作是基于面部动作编码系统（FACS），这是一种为了让人类心理学家从静态图片中编码表情而开发的表示方法。为了避免使用这种启发式编码方案，我们使用我们的计算机视觉系统在实验人群中概率性地表征面部运动和肌肉激活，从而推导出一种新的，更准确的人类面部表情表示，我们称之为FACS+。我们以两种不同的方式使用这种新的表示进行识别。第一种方法直接使用基于物理的模型，通过比较估计的肌肉激活来识别表情。第二种方法使用基于物理的模型来生成针对每个不同表情的整个面部的时空运动能量模板。这些简单的、生物学上合理的运动能量“模板”然后用于识别。这两种方法在表达识别方面都显示出比以前更高的准确性。类别：面部表情，表情识别，面部处理，面部分析，运动和模式分析，基于视觉的人机交互。
We describe a computer vision system for observing facial motion by using an optimal estimation optical flow method coupled with a geometric and a physical (muscle) model describing the facial structure. Our method produces a reliable parametric representation of the face’s independent muscle action groups, as well as an accurate estimate of facial motion. Previous efforts at analysis of facial expression have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static pictures. To avoid use of this heuristic coding scheme, we have used our computer vision system to probabilistically characterize facial motion and muscle activation in an experimental population, thus deriving a new, more accurate representation of human facial expressions that we call FACS+. We use this new representation for recognition in two different ways. The first method uses the physics-based model directly, by recognizing expressions through comparison of estimated muscle activations. The second method uses the physics-based model to generate spatio-temporal motionenergy templates of the whole face for each different expression. These simple, biologically-plausible motion energy “templates” are then used for recognition. Both methods show substantially greater accuracy at expression recognition than has been previously achieved. Categories: Facial Expressions, Expression Recognition, Face Processing, Facial Analysis, Motion and Pattern Analysis, Vision-based HCI.