ITR Collaborative Research: Biometrics -- Performance, Security and Social Impact

ITR 协作研究：生物识别——性能、安全性和社会影响

• 批准号: 0325333
• 负责人: Stephanie Schuckers
• 金额: --
• 依托单位: Clarkson 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: 2004-02-15 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325333&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; Biometrics; Performance

PI: Lawrence A. Hornak, West Virginia UniversityBiometric systems automatically measure a physiological or behavioral "signature" of an individual, from which a decision can be rendered to either authenticate or determine the identity of that individual. In this way, biometric systems provide the means to bind the physical presence of an individual user with her cyber action. Despite the increasing use of these systems, neither an analytic framework for modeling and predicting their performance, nor a set of standard data available to researchers for testing purposes is available. This research addresses these deficiencies by determining an analytic framework in which the performance of biometric systems can be modeled and predicted, removing key barriers to biometric system performance through research in multibiometrics and effective vulnerability countermeasures, and understanding the relationships among biometric applications, privacy, security, and user acceptance that are essential for both informed public policy and system design. Addressing this interrelated set of key technical barriers and societal issues will advance the understanding of biometric system acceptance, performance, and design, and will enable trustworthy authentication and identification functions essential to pervasive computing and supporting homeland security needs. The data sets generated by the coordinated tasks of this research are also being made available as a resource to the biometrics research, developer, and user communities.

PI：劳伦斯·A·霍纳克（Lawrence A. 这样，生物识别系统提供了将单个用户通过网络作用绑定的物理存在的方法。尽管这些系统的使用越来越多，但既不是建模和预测其性能的分析框架，也没有可用于研究人员可用于测试目的的一组标准数据。这项研究通过确定一个分析框架来解决这些缺陷，在该框架中，可以对生物识别系统的性能进行建模和预测，从而通过研究多物体学和有效的脆弱性对策来消除生物识别系统性能的关键障碍，并了解生物识别应用，隐私，安全，安全，安全，安全性，以及对知识公共政策的必不可少的生物识别应用程序之间的关系。解决这一相互关联的关键技术障碍和社会问题将提高对生物识别系统的接受，性能和设计的理解，并将实现值得信赖的身份验证和识别功能，对于普遍计算和支持国土安全需求至关重要。该研究协调任务生成的数据集也可以作为生物识别技术，开发人员和用户社区的资源提供。