The iris of the eye enables one of the most accurate, distinctive, universal, and
re liable 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.