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: 8142075
• 负责人: Aleix M Martinez
• 金额: $ 36.6万
• 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/8142075
• 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; public health relevance; research study; showing emotion; visual process; visual processing

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: Understanding how facial expressions of emotion are processed by our cognitive system will be important for studies of abnormal face and emotion visual processing in schizophrenia, autism and Huntington's disease. Also, abused children are more acute at recognizing emotions, suggesting a higher degree of expertise to some image features. Identifying which features are used by the cognitive system will help develop protocols for reducing their unwanted effects. Understanding the limits in spatial and time resolution will also be important for studies of low vision (acuity), which are typical problems in several eye diseases and in the normal process of aging.

描述（由申请人提供）：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，包括哪些肌肉运动产生最常见的表情。这些面部表情的情感随后被我们的视觉系统解读。然而，人们对如何识别这些面部表情却知之甚少。该提案的总体目标是定义视觉识别中使用的认知（计算）空间的形式和维度。特别是，该提案将研究以下三个假设：虽然面部表情是由一组复杂的肌肉运动产生的，但表情通常可以在不同的空间和时间分辨率下轻松识别。然而，尚不清楚这些限制是什么。我们的第一个假设（H1）是，可以在低分辨率和短曝光时间后实现对情绪面部表情的识别。在目标 1 中，我们定义了实验来确定成功识别不同情绪需要多少像素和毫秒 (ms)。可以在低分辨率下快速识别情感表达的事实表明，采用了对图像处理鲁棒的简单特征。我们的第二个假设（H2）是面部表情情感的识别部分是通过对结构特征的分析来完成的。众所周知，配置线索在其他面部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尚未得到很好的理解。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、这些面部图像的处理版本以及示意图。众所周知，形状特征在面部表情中发挥着重要作用（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与平均（或标准）面孔的偏差，而不是被描述为一组独立的范例（诺斯替神经元）。这个计算空间的重要性不仅在于进一步证明先前目标的结果，而且还在于做出可以通过对人类受试者进行额外实验来验证的新预测。 公共健康相关性：了解我们的认知系统如何处理情绪的面部表情对于研究精神分裂症、自闭症和亨廷顿病的异常面部和情绪视觉处理非常重要。此外，受虐待的儿童在识别情绪方面更加敏锐，这表明他们对某些图像特征具有更高程度的专业知识。识别认知系统使用哪些功能将有助于开发减少其不良影响的协议。了解空间和时间分辨率的限制对于低视力（敏锐度）的研究也很重要，低视力是几种眼部疾病和正常衰老过程中的典型问题。