SaTC: CORE: Small: RUI: Improving Performance of Standoff Iris Recognition Systems Using Deep Learning Frameworks

眼睛的虹膜能够实现最准确、最独特、最通用和最可靠的生物识别方法之一，来验证一个人的身份。然而，虹膜识别的准确性取决于数据采集的质量，而数据采集的质量受视角、遮挡、扩张等因素的影响。由于远距离虹膜识别系统比传统系统的限制要小得多，捕获的虹膜图像可能会偏离角度，放大，或者不太理想。该项目解决了这些具有挑战性的问题，并研究了解决方案，以消除它们对对峙系统的影响。该项目从几个角度提供了潜在的好处：在国家层面，它旨在通过改善虹膜识别的性能来增强美国的国家安全和竞争力，以引领下一代僵持的生物识别系统。在州一级，它提高了阿肯色州的研究和教育质量，阿肯色州是EPSCoR(既定的激励竞争性研究计划)州，并为发展多样化和熟练的劳动力做出了贡献。在大学层面，它为来自代表性不足群体的学生提供研究机会，并为他们提供宝贵的技能来建立他们的职业生涯，包括创造力、自信、批判性思维和解决问题。该项目旨在通过在传统和非传统虹膜识别框架内使用深度学习技术来提高远端虹膜识别的性能。首先，提出了一种基于深度学习的正面图像重建框架，以消除眼睛结构对正面图像的影响，然后将这些图像与数据库中的正面图像进行比对。它将在传统的虹膜识别框架内使用非线性失真映射和遮挡面具来解开非理想的虹膜图像。其次，开发了基于深度学习算法的非传统虹膜识别框架，利用眼部和眼周结构中的附加生物特征信息来提高对峙系统的性能。该方法还调查了虹膜/眼睛/眼周生物识别中凝视角度的影响，并结合了不同距离图像中的生物识别信息。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The iris of the eye enables one of the most accurate, distinctive, universal, and reliable biometrics for authenticating the identity of a person. However, the accuracy of iris recognition depends on the quality of data acquisition, which is negatively affected by the angle of view, occlusion, dilation, and other factors. Since standoff iris recognition systems are much less constrained than traditional systems, the captured iris images are likely to be off-angle, dilated, and otherwise less than ideal. This project addresses these challenging problems and investigates solutions to eliminate their effects on standoff systems. The project provides potential benefits from several perspectives: At the national level, it aims to enhance the national security and competitiveness of the United States by improving the performance of iris recognition to lead the next generation of standoff biometrics systems. At the state level, it improves the quality of research and education in Arkansas, an EPSCoR (Established Program to Stimulate Competitive Research) state, and contributes to the development of a diverse and skilled workforce. At the university level, it provides research opportunities for students from underrepresented groups and equips them with valuable skills to build their careers including creativity, self-confidence, critical thinking and problem solving.This project aims to improve the performance of standoff iris recognition using deep learning techniques within both traditional and nontraditional iris recognition frameworks. First, a deep learning-based frontal image reconstruction framework is developed to eliminate the effect of the eye structures on standoff images before comparing these images with their frontal images in a database. It will unwrap non-ideal iris images within the traditional iris recognition framework using non-linear distortion maps and occlusion masks. Second, nontraditional iris recognition frameworks are developed based on deep learning algorithms to improve the performance of standoff systems using additional biometric information in ocular and periocular structures. This approach also investigates the effect of the gaze angle in iris/ocular/periocular biometrics and combines the biometric information in different standoff images.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.