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.
Matching iris samples based on Daugman's approach is an operation that involves the accumulation of bitwise XOR operations between the biometric templates, and can be done quite efficiently. However, the computational complexity of the task grows linearly with the increase in enrollment size, and the complexity for tasks like de-duplication grows quadratically regarding the size of the database, as noted by @cite_4 .
@cite_4
2032886922
Most of the methods to index iris biometric signatures were designed for decision environments with a clear separation between genuine and impostor matching scores. However, in case of less controlled data acquisition, images will be degraded and the decision environments poorly separated. This paper proposes an indexing retrieval method for degraded images and operates at the code level, making it compatible with different feature encoding strategies. Gallery codes are decomposed at multiple scales, and according to their most reliable components at each scale, the position in an n-ary tree determined. In retrieval, the probe is decomposed similarly, and the distances to multiscale centroids are used to penalize paths in the tree. At the end, only a subset of the branches is traversed up to the last level. When compared with related strategies, the proposed method outperforms them on degraded data, particularly in the performance range most important for biometrics . Finally, according to the computational cost of the retrieval phase, the number of enrolled identities above which indexing is computationally cheaper than an exhaustive search is determined.