I-Corps: Exploiting matching score distributions to improve biometric recognition

I-Corps：利用匹配分数分布来提高生物特征识别

• 批准号: 1561151
• 负责人: Ioannis Kakadiaris
• 金额: $ 5万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-15 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561151&HistoricalAwards=false
• 关键词: Corps; Exploiting; matching; score; distributions

Whether we want to unlock our cell phones or pay with our credit cards, we need to verify our identity. Biometric technologies can help accomplish this task easier, faster and in a more secure manner. For example, Apple's Touch ID system allows users to unlock their phones and make payments using their fingerprint. However, conditions such as noise in the background (in the case of using speech) or bad illumination (in the case of using facial images) can degrade the accuracy of these systems. This problem is more pronounced for challenging recognition tasks such as surveillance, access control, and personalized customer service. Acuity?s latest report estimates that $20 billion in annual revenues will be generated in the biometrics sector from direct purchase and software development fees by the end of 2020. The proposed add-on software developed by this I-Corps team can improve the system's decision-making process regardless of the biometric train used (e.g., face, speech, or iris). Potential customers can either add the proposed software to their biometric system or use a cloud-based app.Recognition algorithms compare biometric data (e.g., speech segments or facial images) to produce scores that reflect how similar are these samples. However, poor acquisition conditions degrade the quality of the obtained scores. This problem affects all biometric systems regardless of the biometric trait used. Score normalization methods transform scores into a domain that reflects similarity more accurately. This team has developed a framework that describes how to employ existing methods (and those to be invented) more effectively. Specifically, the proposed invention is an algorithm that partitions a set of scores into subsets and then normalizes the scores of each subset independently. Participating in the NSF I-Corps program will allow the team to test its hypotheses and improve its chances of converting companies to clients. At the end of the program, the team intends to present a refined business model canvas and demonstrate its minimum viable product.

无论是要解锁手机还是用信用卡付款，我们都需要验证我们的身份。生物识别技术可以更轻松，更安全，更安全地帮助完成此任务。例如，Apple的触摸ID系统允许用户使用指纹解锁手机并付款。但是，背景中的噪声（如果使用语音）或不良照明（在使用面部图像的情况下）可以降低这些系统的准确性。对于诸如监视，访问控制和个性化客户服务之类的挑战性识别任务，此问题更为明显。敏锐度的最新报告估计，生物识别领域将从直接购买和软件开发费用到2020年底在生物识别部门中产生200亿美元的收入。该I-Corps团队开发的拟议附加软件可以改善系统的决策过程，而不管使用的生物识别列表使用了什么（例如，面部，演讲，语音，或IRIS）。潜在客户可以将提出的软件添加到其生物识别系统中，也可以使用基于云的应用程序。识别算法比较生物识别数据（例如，语音片段或面部图像），以产生反映这些样本的相似程度的分数。但是，获取条件差会降低所获得的分数的质量。无论使用的生物特征性质如何，此问题都会影响所有生物识别系统。得分归一化方法将得分转化为更准确地反映相似性的域。该团队开发了一个框架，该框架描述了如何更有效地采用现有方法（以及要发明的方法）。具体而言，提出的发明是一种算法，该算法将一组分数分配到子集中，然后独立地将每个子集的得分归一化。参加NSF I-Corps计划将使团队能够检验其假设并提高将公司转换为客户的机会。在计划结束时，团队打算呈现精致的商业模式画布，并展示其最低可行产品。