Segmentation-Aware and Adaptive Iris Recognition
- URL: http://arxiv.org/abs/2001.00989v1
- Date: Tue, 31 Dec 2019 04:31:37 GMT
- Title: Segmentation-Aware and Adaptive Iris Recognition
- Authors: Kuo Wang, Ajay Kumar
- Abstract summary: The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris matching accuracy.
The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios.
This paper presents such a segmentation-assisted adaptive framework for more accurate less-constrained iris recognition.
- Score: 24.125681602124477
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Iris recognition has emerged as one of the most accurate and convenient
biometric for the human identification and has been increasingly employed in a
wide range of e-security applications. The quality of iris images acquired
at-a-distance or under less constrained imaging environments is known to
degrade the iris matching accuracy. The periocular information is inherently
embedded in such iris images and can be exploited to assist in the iris
recognition under such non-ideal scenarios. Our analysis of such iris templates
also indicates significant degradation and reduction in the region of interest,
where the iris recognition can benefit from a similarity distance that can
consider importance of different binary bits, instead of the direct use of
Hamming distance in the literature. Periocular information can be dynamically
reinforced, by incorporating the differences in the effective area of available
iris regions, for more accurate iris recognition. This paper presents such a
segmentation-assisted adaptive framework for more accurate less-constrained
iris recognition. The effectiveness of this framework is evaluated on three
publicly available iris databases using within-dataset and cross-dataset
performance evaluation and validates the merit of the proposed iris recognition
framework.
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