EAGER: RI: Developing a Framework for Automated Measurement of the Intensity of Spontaneous Facial Expressions

EAGER：RI：开发自动测量自发面部表情强度的框架

• 批准号: 0957983
• 负责人: Mohammad Mahoor
• 金额: $ 7.99万
• 依托单位: University of Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-11-01 至 2011-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0957983&HistoricalAwards=false
• 关键词: EAGER; RI; Developing; Framework; Automated

Spontaneous facial expressions are representative of facial expressions in daily life. Studying spontaneous (non-posed) facial expressions and Facial Action Coding System-AUs (FACS-AUs) requires spontaneous face expression data where the action units are manually and reliably coded. Currently, there is a high demand in computer vision society for such a database. The first objective of this research is to capture a 3D facial expression database and make it publically available to others interested in studying spontaneous facial expressions. As ground-truth, we also manually code the existence and the intensity of important action units (i.e., AU 1, 2, 4, 6, 9, 10, 12, 14, 15, 20, 25, and 26) in this dataset. These action units describe positive and negative emotional expressions. The second objective of this research is to develop a framework to automatically measure the intensity of spontaneous FACS action units in videos captured from adults? faces. We aim to study dictionary-based representation (e.g., combination of wavelet and shearlet) to model face appearance in facial images. This is due to the fact that human facial images contain of edges, wrinkles and texture. We study this type of combined representations to provide a sparse representation of images containing edges and textures and utilize it in measuring the AU intensities. The results of this research, the face database, the manually codes of AUs, and the developed framework in the measuring the AUs, are made publically available to other researchers in computer vision society.

自发性面部表情是日常生活中面部表情的代表。研究自发（非姿势）面部表情和面部动作编码系统-AU（FACS-AU）需要自发面部表情数据，其中动作单元被手动和可靠地编码。目前，计算机视觉社会对这样的数据库有很高的需求。本研究的第一个目标是捕获一个3D面部表情数据库，并使其可用于其他有兴趣研究自发面部表情。作为地面实况，我们还手动编码重要动作单元的存在和强度（即，Au 1、2、4、6、9、10、12、14、15、20、25和26）。这些行动单位描述了积极和消极的情绪表达。本研究的第二个目标是开发一个框架，自动测量的强度自发的流式细胞仪行动单位的视频捕捉成人？脸上我们的目标是研究基于字典的表示（例如，小波和剪切波的组合）来对面部图像中的面部外观进行建模。这是由于人脸图像包含边缘、皱纹和纹理的事实。我们研究这种类型的组合表示，以提供一个稀疏表示的图像包含边缘和纹理，并利用它在测量的Au强度。本研究的成果，人脸数据库，人工编码的AU，和开发的框架，在测量AU，提供给其他研究人员在计算机视觉社会。