Found a good match: should I keep searching? - Accuracy and Performance in Iris Matching Using 1-to-First Search

TL;DR

This paper evaluates the accuracy and efficiency of 1:First iris search compared to traditional 1:N search, showing trade-offs between speed and accuracy across various scenarios.

Contribution

It provides a comprehensive analysis of 1:First search performance in iris recognition, identifying conditions where it maintains accuracy while reducing search time.

Findings

1:First search reduces search time significantly.

Accuracy degrades with 1:First but can be acceptable under certain parameters.

Different scenarios confirm the trade-off between speed and accuracy.

Abstract

Iris recognition is used in many applications around the world, with enrollment sizes as large as over one billion persons in India's Aadhaar program. Large enrollment sizes can require special optimizations in order to achieve fast database searches. One such optimization that has been used in some operational scenarios is 1:First search. In this approach, instead of scanning the entire database, the search is terminated when the first sufficiently good match is found. This saves time, but ignores potentially better matches that may exist in the unexamined portion of the enrollments. At least one prominent and successful border-crossing program used this approach for nearly a decade, in order to allow users a fast "token-free" search. Our work investigates the search accuracy of 1:First and compares it to the traditional 1:N search. Several different scenarios are considered trying to emulate real environments as best as possible: a range of enrollment sizes, closed- and open-set configurations, two iris matchers, and different permutations of the galleries. Results confirm the expected accuracy degradation using 1:First search, and also allow us to identify acceptable working parameters where significant search time reduction is achieved, while maintaining accuracy similar to 1:N search.