A Study of the Computational Space of Facial Expressions of Emotion

项目摘要 过去的研究非常成功地定义了面部表情是如何产生的，包括 哪种肌肉运动创造了最常见的表情。这些情绪的面部表情 然后由我们的视觉系统解释。然而，人们对这些面部表情是如何表现的知之甚少。 被认可了。这一提议的首要目标是定义认知的形式和维度 (计算)在视觉识别中使用的空间。特别是，这项建议将研究以下三个方面 假设：虽然面部表情是由一系列复杂的肌肉运动产生的，但表情 在不同的空间和时间分辨率下通常很容易识别。然而，目前还不知道这些是什么 是有限度的。我们的第一个假设(H1)是，面部表情的识别可以以较低的速度实现 分辨率和短曝光时间后。在目标1中，我们定义实验来确定有多少像素 并且需要毫秒(Ms)来成功识别不同的情绪。事实是，表达 在低分辨率下可以快速识别情感，这表明简单的特征对图像是稳健的 使用了操纵手段。我们的第二个假设(H2)是面部表情的识别 情感部分是通过对结构特征的分析来实现的。已知的组态提示会对 在其他人脸识别任务中起重要作用，但它们在情绪表达的处理中的作用不是 然而，这一点很好理解。目标2将识别一些这样的配置线索。我们将使用真实的人脸图像， 这些人脸图像和原理图的经过处理的版本。还众所周知，形状特征起作用 面部表情中的一个角色(例如，快乐时嘴巴的弯曲)。在目标3中，我们定义了一个基于形状的 计算模型。我们的假设(H3)是将结构和形状特征定义为偏差 来自一张中庸(或规范)的面孔，而不是被描述为一组独立的样本(诺斯替 神经元)。这种计算空间的重要性不仅是为了进一步证明之前 目的是为了做出新的预测，这些预测可以通过对人类受试者的额外实验来验证。

Project Summary Past research has been very successful in defining how facial expressions of emotion are produced, including which muscle movements create the most commonly seen expressions. These facial expressions of emotion are then interpreted by our visual system. Yet, little is known about how these facial expressions are recognized. The overarching goal of this proposal is to define the form and dimensions of the cognitive (computational) space used in this visual recognition. In particular, this proposal will study the following three hypotheses: Although facial expressions are produced by a complex set of muscle movements, expressions are generally easily identified at different spatial and time resolutions. However, it is not know what these limits are. Our first hypothesis (H1) is that recognition of facial expressions of emotion can be achieved at low resolutions and after short exposure times. In Aim 1, we define experiments to determine how many pixels and milliseconds (ms) are needed to successfully identify different emotions. The fact that expressions of emotion can be recognized quickly at low resolution indicates that simple features robust to image manipulation are employed. Our second hypothesis (H2) is that the recognition of facial expressions of emotion is partially accomplished by an analysis of configural features. Configural cues are known to play an important role in other face recognition tasks, but their role in the processing of expressions of emotion is not yet well understood. Aim 2 will identify a number of these configural cues. We will use real images of faces, manipulated versions of these face images, and schematic drawings. It is also known that shape features play a role in facial expressions (e.g., the curvature of the mouth in happiness). In Aim 3, we define a shape-based computational model. Our hypothesis (H3) is that the configural and shape features are defined as deviations from a mean (or norm) face as opposed to being described as a set of independent exemplars (Gnostic neurons). The importance of this computational space is not only to further justify the results of the previous aims, but to make new predictions that can be verified with additional experiments with human subjects.