A Study of the Computational Space of Facial Expressions of Emotion

面部表情情感的计算空间研究

• 批准号: 8494053
• 负责人: Aleix M Martinez
• 金额: $ 34.77万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8494053
• 关键词: Acute; Address; Affect; Aging-Related Process; Anger; Arts; Autistic Disorder; Behavior; Child Abuse; Classification; Code; Cognition; Cognitive; Complex; Computer Simulation; Computer Vision Systems; Conscious; Cues; Depressed mood; Dimensions; Duchenne muscular dystrophy; Emotions; Evolution; Eye diseases; Face; Face Processing; Facial Expression; Facial Muscles; Fright; Goals; Happiness; Human; Huntington Disease; Image; Individual; Lead; Movement; Muscle; Neurons; Oral cavity; Perception; Play; Positioning Attribute; Primates; Process; Protocols documentation; Research; Resolution; Role; Schizophrenia; Shapes; Social Interaction; System; Time; To specify; Visual; Visual impairment; Visual system structure; base; cognitive system; computer human interaction; computer studies; court; design; human subject; millisecond; psychologic; research study; showing emotion; visual process; visual processing

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.

项目概要 过去的研究在定义情绪的面部表情是如何产生的方面非常成功，包括 哪些肌肉运动会产生最常见的表情。这些情绪的表情 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达的却知之甚少。 认可。该提案的总体目标是定义认知的形式和维度 此视觉识别中使用的（计算）空间。具体来说，本提案将研究以下三个方面 假设：虽然面部表情是由一组复杂的肌肉运动产生的，但表情 通常在不同的空间和时间分辨率下很容易识别。然而，不知道这些是什么 限制是。我们的第一个假设（H1）是，情绪面部表情的识别可以在较低的条件下实现。 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 成功识别不同情绪需要毫秒（ms）。事实上，表达式 可以在低分辨率下快速识别情感表明简单的特征对图像具有鲁棒性 采用操纵。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完成的。众所周知，配置提示可以发挥作用 在其他人脸识别任务中发挥着重要作用，但它们在情绪表达处理中的作用并不重要 但很好理解。目标 2 将识别许多这样的配置线索。我们将使用真实的面部图像， 这些面部图像和示意图的操纵版本。众所周知，形状特征发挥着 在面部表情中的作用（例如，幸福时嘴部的弧度）。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）是配置和形状特征被定义为偏差 来自平均（或规范）面孔，而不是被描述为一组独立的范例（诺斯替教 神经元）。这个计算空间的重要性不仅在于进一步证明之前的结果 目标，而是做出新的预测，可以通过对人类受试者进行额外的实验来验证。