Uniqueness of Iris Pattern Based on AR Model
Katelyn M. Hampel Jinyu Zuo Priyanka Das Natalia A. Schmid Stephanie Schuckers Joseph Skufca Matthew C. Valenti
The assessment of iris uniqueness plays a crucial role in analyzing the
capabilities and limitations of iris recognition systems. Among the various
methodologies proposed, Daugman's approach to iris uniqueness stands out as one
of the most widely accepted. According to Daugman, uniqueness refers to the
iris recognition system's ability to enroll an increasing number of classes
while maintaining a near-zero probability of collision between new and enrolled
classes. Daugman's approach involves creating distinct IrisCode templates for
each iris class within the system and evaluating the sustainable population
under a fixed Hamming distance between codewords. In our previous work [23], we
utilized Rate-Distortion Theory (as it pertains to the limits of
error-correction codes) to establish boundaries for the maximum possible
population of iris classes supported by Daugman's IrisCode, given the
constraint of a fixed Hamming distance between codewords. Building upon that
research, we propose a novel methodology to evaluate the scalability of an iris
recognition system, while also measuring iris quality. We achieve this by
employing a sphere-packing bound for Gaussian codewords and adopting a approach
similar to Daugman's, which utilizes relative entropy as a distance measure
between iris classes. To demonstrate the efficacy of our methodology, we
illustrate its application on two small datasets of iris images. We determine
the sustainable maximum population for each dataset based on the quality of the
images. By providing these illustrations, we aim to assist researchers in
comprehending the limitations inherent in their recognition systems, depending
on the quality of their iris databases.