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.

项目摘要 过去的研究一直非常成功地定义了如何产生情感的面部表情，包括 哪些肌肉运动会产生最常见的表情。这些面部表情 然后由我们的视觉系统解释。然而，对于这些面部表情的方式知之甚少 认可。该提案的总体目标是定义认知的形式和尺寸 （计算）此视觉识别中使用的空间。特别是，该建议将研究以下三个 假设：尽管面部表情是由一组复杂的肌肉运动产生的，但表达式 通常在不同的空间和时间分辨率下轻松识别。但是，不知道这些 限制是。我们的第一个假设（H1）是，可以在低处实现对情感表情的认识 分辨率和短期曝光时间后。在AIM 1中，我们定义实验以确定多少像素 需要成功识别不同情绪的毫秒（MS）。表达的事实 可以在低分辨率下快速识别情绪，表明简单的功能可靠地形象 操作被采用。我们的第二个假设（H2）是对面部表情的认识 情感是通过对配置特征的分析来部分实现的。众所周知，配置提示会播放 在其他面部识别任务中的重要作用，但是它们在情感表达的处理中的作用不是 但是很理解。 AIM 2将确定许​​多这些配置提示。我们将使用面部的真实图像， 这些面部图像和示意图的操纵版本。还知道形状功能播放 在面部表情中的作用（例如，在幸福中嘴巴的曲率）。在AIM 3中，我们定义了基于形状的 计算模型。我们的假设（H3）是将配置和形状特征定义为偏差 从平均（或规范）的面前而不是被描述为一组独立示例（诺斯替教） 神经元）。该计算空间的重要性不仅是为了进一步证明先前的结果 目的，但要通过对人类受试者进行的其他实验来进行新的预测。