The School of Data Science and Analytics is pleased to announce the dissertation defense of Mr. Jitendra Sai Kota on Friday, July 18th at 10:00 a.m. Please join us in person in Atrium J1112 or via Teams (Link HERE).
 

Mr. Kota’s dissertation is entitled “Enhancing Off-Angle Iris Recognition Performance”.

His committee is: Dr. Mahmut Karakaya (chair), Dr. Ramazan Aygun, and Dr. Md. Abdullah Al Hafiz Khan.

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Abstract:

The usage of biometrics offers a reliable and high performing technology in the field of person identification and verification. Iris recognition, which uses the human iris as the biometric, is known for its low False Acceptance Rates and False Rejection Rates, making it one of the most secure and accurate forms of biometric authentication available today. However, most of the current iris recognition systems are based on frontal iris recognition, which limits the usability of the technology.

In frontal iris recognition, it is required that the axis of the human eye aligns with the axis of camera lens. To make iris recognition unconstrained and expand on its usability, there is a need for off-angle iris recognition. In off-angle iris recognition, there is no need for the axis match of human eye and camera lens, making off-angle iris recognition suitable for tasks like fast security checks and remote surveillance. However, off-angle iris recognition is not as simple as performing a perspective projection, because of several factors such as illumination, presence of cornea and limbus in the human eye. In this thesis, we propose three models that aim at improving the off-angle iris recognition at different levels. The first model identifies the most affected regions in the iris images and masks them. The second model creates a frontal projection of the off-angle iris image using a modified Pix2Pix GAN model. The third model proposes a novel segmentation algorithm for off-angle iris images.