Open AccessBloom filter‐based search structures for indexing and retrieving iris‐codes
Author(s) -
Drozdowski Pawel,
Rathgeb Christian,
Busch Christoph
Publication year - 2018
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2017.0007
Subject(s) - bloom filter , biometrics , computer science , search engine indexing , scalability , workload , iris recognition , identification (biology) , benchmark (surveying) , data mining , filter (signal processing) , machine learning , artificial intelligence , database , computer vision , algorithm , botany , geodesy , biology , geography , operating system
Large‐scale biometric deployments are becoming ubiquitous. The computational workload of the conventional retrieval method, requiring 1: N comparisons in the identification mode, is impractical for such systems. In recent years, many approaches for efficient biometric identification were proposed, but their scalability is often questionable. Furthermore, the lack of a unified methodology for biometric workload reduction reporting often makes a direct benchmark or a thorough evaluation of the proposed schemes cumbersome. We present an iris indexing scheme based on Bloom filters and binary search trees. With a statistical model, the system is shown to be theoretically scalable for arbitrarily many enrollees. We evaluate this system on a combined database from several publicly available datasets, containing a total of 11,936 iris images from 1477 instances. In an open‐set identification scenario, the system maintains the biometric performance of an iris‐code 1: N baseline – a true positive identification rate of approximately 98% measured at 0.1% false positive identification rate, at only 10% of the baseline workload. In a proof‐of‐concept multi‐iris indexing experiment, the true positive identification rate is further increased to over 99%, without additional workload costs. Lastly, we define several prerequisites necessary for a transparent and comprehensive methodology of biometric workload reduction results dissemination.