The human iris exhibits random and unique textural patterns that allow for identification with high accuracy. These patterns are evident in near-infrared (NIR) imagery, even for very dark irises. Iris templates are created from NIR iris imagery with the Ridge Energy Direction (RED) recognition algorithm and subsequently matched to measure performance. In this paper we investigate the feasibility of algorithm optimization by performing an initial fractional template comparison to eliminate high Hamming distance (HD) score matches followed by a full template re-comparison or iterative higher fractional template comparison on remaining templates. Recognition performance for different fractional template areas is analyzed with a view towards substantial improvement of algorithm identification time performance. The feasibility of identification time reduction by 60% or more is reported both ICE and Bath data sets.

The unique patterns of complex texture that are visible in iris images captured under near-infrared illumination are highly stable and can be used for high confidence biometric recognition. Similar patterns are visible in lighter colored irises under visible light. This paper presents an analysis of a database of iris images captured using multispectral illumination, from 405 nm to 1070 nm in wavelength. The analysis is based on matching performance using a Daugman-based commercial implementation of the iris recognition algorithm. We find that illumination wavelength has a very significant effect on iris recognition performance.

The human iris exhibits random and unique textural patterns that allow for identification with high accuracy. These patterns are evident in near-infrared (NIR) imagery, even for very dark irises. The authors investigate the information content of the iris contained in these patterns, and how it affects recognition performance. In this paper, iris templates are created from NIR iris imagery with the Ridge Energy Direction (RED) recognition algorithm, and using common biometric performance metrics we determine which portions of the iris contain the most distinctive information for recognition.