Feasibility of iris identification algorithm optimization by fractional template matching

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.