Incremental iris recognition: A single-algorithm serial fusion strategy to optimize time complexity

Daugman´s algorithm, mapping iris images to binary codes and estimating similarity between codes applying the fractional Hamming Distance, forms the basis of today´s commercially used iris recognition systems. However, when applied to large-scale databases, the linear matching of a single extracted iris-code against a gallery of templates is very time consuming and a bottleneck of current implementations. As an alternative to pre-screening techniques, our work is the first to present an incremental approach to iris recognition. We combine concentration of information in the first bits of an iris-code with early rejection of unlikely matches during matching stage to incrementally determine the best-matching candidate in the gallery. Our approach can transparently be applied to any iris-code based system and is able to reduce bit comparisons significantly (to about 5% of iris-code bits) while exhibiting a Rank-1 Recognition Rate being at least as high as for matches involving all bits.