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

无论我们是想解锁手机还是用信用卡支付，我们都需要验证自己的身份。生物识别技术可以帮助更容易、更快、更安全地完成这项任务。例如，苹果的Touch ID系统允许用户解锁手机，并使用指纹进行支付。然而，背景中的噪声(在使用语音的情况下)或较差的照明(在使用面部图像的情况下)等条件会降低这些系统的准确性。对于监控、访问控制和个性化客户服务等具有挑战性的识别任务，这个问题更加明显。敏锐度？S最新报告估计，到2020年底，生物识别领域的直接购买和软件开发费将产生200亿美元的年收入。由i-Corps团队开发的拟议附加软件可以改进系统的决策过程，而不考虑使用的生物识别序列(例如，人脸、语音或虹膜)。潜在客户可以将建议的软件添加到他们的生物识别系统中，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相似性程度的分数。然而，糟糕的获取条件会降低获得的分数的质量。此问题影响所有生物识别系统，而不考虑所使用的生物识别特征。分数归一化方法将分数转换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使用现有方法(以及将要发明的方法)。具体地说，所提出的发明是一种将分数集合划分为子集，然后独立地归一化每个子集的分数的算法。参与NSF i-Corps计划将使该团队能够检验其假设，并提高将公司转化为客户的机会。在计划结束时，团队打算展示一个完善的商业模式画布，并展示其最低限度的可行产品。

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.