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: 7946918
• 负责人: Aleix M Martinez
• 金额: $ 28.59万
• 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/7946918
• 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

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）：过去的研究已经非常成功地定义了情绪的面部表情是如何产生的，包括哪些肌肉运动创造了最常见的表情。这些情绪的面部表情然后被我们的视觉系统解释。然而，人们对这些面部表情是如何被识别的知之甚少。这个提议的首要目标是定义在这种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，但表情通常很容易在不同的空间和时间分辨率下识别。但是，我们不知道这些限制是什么。我们的第一个假设（H1）是，面部表情的情感识别可以在低分辨率和短曝光时间后实现。在目标1中，我们定义了实验来确定需要多少像素和毫秒（ms）来成功识别不同的情绪。情感的表达可以在低分辨率下快速识别的事实表明，采用对图像操作鲁棒的简单特征。我们的第二个假设（H2）是面部表情的情感识别部分是通过分析面部特征来完成的。已知的配置线索在其他人脸识别任务中起着重要的作用，但它们在情绪表达过程中的作用还没有得到很好的理解。目标2将识别出许多这样的神经线索。我们将使用人脸的真实的图像，这些人脸图像的操纵版本，以及示意图。还已知的是，形状特征在面部表情中起作用（例如，幸福时嘴角的弧度）。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，面部和形状特征被定义为与平均（或范数）面部的偏差，而不是被描述为一组独立的样本（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。 公共卫生相关性：了解情绪的面部表情是如何被我们的认知系统处理的，对于研究精神分裂症、孤独症和亨廷顿病的异常面部和情绪视觉加工具有重要意义。此外，受虐儿童在识别情绪方面更敏锐，这表明他们对某些图像特征的专业程度更高。识别哪些特征被认知系统使用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，这是几种眼科疾病和正常衰老过程中的典型问题。