PERCEPTUAL LEARNING THEORY OF THE INFORMATION IN FACES

面部信息知觉学习理论

• 批准号: 2251149
• 负责人: Alice J O'Toole
• 金额: $ 11.19万
• 依托单位: UNIVERSITY OF TEXAS DALLAS
• 依托单位国家: 美国
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-30 至 1999-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2251149
• 关键词: association learning; computer simulation; face expression; gender difference; human age group; human subject; information theory; memory; psychological models; visual perception

With ease human observers can recognize and identify familiar faces as well as extract additional information from both familiar and unfamiliar faces, including the sex, approximate age, race, and current emotional state of the person. Nevertheless, faces pose challenging computational problems for the perceiver. They are highly similar to one another, containing the same features arranged in roughly the same configuration. Perceivers must, therefore, be able to encode very subtle variations in the form and configuration of facial features. We develop a quantifiable theory of the perceptual information in faces and model the learning of this information. Faces are represented using "features" derived from the statistical structure of a set of learned faces, and the information most useful for discriminating among faces emerges as an optimal code. Our theory is implemented as a computational autoassociative memory (computer simulation) that operates on image-based codings of faces. The memory represents faces as a weighted sum of the eigenvectors (principal components, "features") of a covariance matrix of learned face images; these facial features may be displayed visually and are useful for both face recognition and visually-derived semantic categorizations of faces. We believe many face processing tasks and empirical phenomena are constrained more by perceptual factors than by complicated cognitive and semantic ones. Hence, our primary goal is to determine the extent to which perceptual constraints alone can account for these tasks and phenomena. As it is beyond the scope of the present proposal to examine all such phenomena, we have chosen a diverse subset. Our strategy in each case will be (a) to relate model-predicted accuracy and facial characteristic ratings to human measures of the same at the level of individual faces and (b) to alter face images synthetically so as to alter accuracy or ratings in predictable ways for human observers viewing the same set of faces processed by the autoassociative memory. We will address three issues: (a) typicality --more typical faces are less well recognized; (b) the perception of the sex of faces -- we model the structural differences between male and female faces and relate them to human ratings/performance using sex-linked facial characteristics; (c) the quantification and perception of the age of a face. Finally, we will analyze the eigenvectors in basic visual processing terms and compare the quality of face representations that emerge from principal components analysis as a function of spatial scale.

人类观察者可以轻松地识别和识别熟悉的面孔， 以及从熟悉和不熟悉的信息中 面孔，包括性别、大致年龄、种族和当前情绪 人的状态。尽管如此，人脸对计算 对感知者的困扰。它们彼此之间高度相似， 包含以大致相同的配置布置的相同特征。 因此，感知者必须能够对非常微妙的变化进行编码， 面部特征的形状和结构。我们开发了一个可量化的 理论的知觉信息的面孔和模型的学习 的该项目海外面使用从 统计结构的一组学习的面孔，和信息最 用于在面部之间进行区分的最佳代码出现。我们 理论被实现为计算自联想存储器（计算机 模拟），其对面部的基于图像的编码进行操作。存储器 将面表示为特征向量（主向量）的加权和 分量，“特征”）; 这些面部特征可以可视地显示 面部识别和面部的视觉导出的语义分类。 我们相信，许多面孔加工任务和经验现象是 更多的是受知觉因素的制约，而不是受复杂的认知和 语义的。因此，我们的主要目标是确定 知觉约束本身就可以解释这些任务和现象。作为 研究所有这些问题超出了本建议的范围， 现象，我们选择了一个不同的子集。我们在每种情况下的战略将 是（a）将模型预测的准确性和面部特征 在个人面部水平上对人类测量的评级， (b)合成地改变面部图像以改变准确性或评级 对于观察同一组面孔的人类观察者来说， 由自联想记忆处理。我们将讨论三个问题：（a） 典型性--更典型的面孔不太容易识别;（B） 对面孔性别的感知--我们模拟了 男性和女性面孔之间的差异，并将其与人类评级/表现联系起来 使用与性别相关的面部特征;（c）量化和 一张脸的年龄。最后，我们将分析特征向量 在基本的视觉处理方面， 从主成分分析中出现的表示， 空间尺度的功能。