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