Recognizing Disguised Faces

识别伪装面孔

• 批准号: 0339122
• 负责人: Michael Tarr
• 金额: --
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-15 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0339122&HistoricalAwards=false
• 关键词: Recognizing; Disguised; Faces

Observers are amazingly proficient at identifying human faces under different viewing conditions and across time. However, such variations are not intentional attempts to change the appearance of an individual. With funds from NSF, Dr. Michael J. Tarr and Yi Cheng are investigating how disguised faces are recognized. That is, are we equally good at recognizing someone when they try to hide their identity? And if not, can we train observers to do better? The investigators are addressing these questions by exploring the perceptual mechanisms used in recognizing faces that have been disguised. They use an innovative combination of computer graphics, computational modeling, human psychophysics, and neuroimaging to study the underlying cognitive and neural mechanisms used by human observers to achieve the invariant recognition of individual faces. This project will also identify the conditions under which we are prone to fail in recognizing a familiar face, because of how it has been disguised. Given such information, the goal is to develop training protocols that improve observers' ability to identify those individuals who are attempting to mask their identity. The intellectual merits of this research arise from improved understanding of human face recognition abilities, particularly in a rarely studied domain. Broader impacts of this project include the creation of a standardized "face database" available to the scientific community, consisting of multiple races for use in both behavioral and computational studies. Broader impacts also include increased knowledge of how to train observers (and possibly computational algorithms) to detect disguised individuals. This research will also involve several Brown undergraduates interested in pursuing scientific careers.

观察者在不同的观察条件下和不同的时间内都能熟练地识别人脸。然而，这样的变化并不是有意地试图改变个体的外观。在NSF的资助下，Michael J. Tarr博士和Yi Cheng正在研究如何识别伪装的面孔。也就是说，当一个人试图隐藏自己的身份时，我们是否同样善于识别他？如果没有，我们能训练观察者做得更好吗？研究人员正在通过探索用于识别伪装面孔的感知机制来解决这些问题。他们使用计算机图形学，计算建模，人类心理物理学和神经成像的创新组合来研究人类观察者用于实现个体面部不变识别的潜在认知和神经机制。这个项目还将确定在哪些情况下，我们很容易无法识别一个熟悉的面孔，因为它是如何伪装的。有了这些信息，目标是制定培训方案，提高观察员识别试图掩盖身份者的能力。这项研究的智力价值来自于对人类面部识别能力的更好理解，特别是在一个很少研究的领域。该项目的更广泛影响包括创建一个标准化的“人脸数据库”，供科学界使用，包括用于行为和计算研究的多个种族。更广泛的影响还包括如何训练观察者（可能还有计算算法）来发现伪装的个人。这项研究还将涉及几个布朗大学的本科生有兴趣追求科学事业。