Biometric Identification over Encrypted Data Made Feasible
ICISS '09 Proceedings of the 5th International Conference on Information Systems Security
Parallelizing iris recognition
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
IEEE Transactions on Image Processing
Extending match-on-card to local biometric identification
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Error-tolerant searchable encryption
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Negative databases for biometric data
Proceedings of the 12th ACM workshop on Multimedia and security
Coarse indexing of iris database based on iris colour
International Journal of Biometrics
Iris recognition based on robust iris segmentation and image enhancement
International Journal of Biometrics
An efficient indexing scheme for iris biometric using k-d-b trees
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Local feature based retrieval approach for iris biometrics
Frontiers of Computer Science: Selected Publications from Chinese Universities
Consistency analysis on orientation features for fast and accurate palmprint identification
Information Sciences: an International Journal
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In this paper, we propose a fast search algorithm for a large fuzzy database that stores iris codes or data with a similar binary structure. The fuzzy nature of iris codes and their high dimensionality render many modern search algorithms, mainly relying on sorting and hashing, inadequate. The algorithm that is used in all current public deployments of iris recognition is based on a brute force exhaustive search through a database of iris codes, looking for a match that is close enough. Our new technique, Beacon Guided Search (BGS), tackles this problem by dispersing a multitude of ldquobeaconsrdquo in the search space. Despite random bit errors, iris codes from the same eye are more likely to collide with the same beacons than those from different eyes. By counting the number of collisions, BGS shrinks the search range dramatically with a negligible loss of precision. We evaluate this technique using 632,500 iris codes enrolled in the United Arab Emirates (UAE) border control system, showing a substantial improvement in search speed with a negligible loss of accuracy. In addition, we demonstrate that the empirical results match theoretical predictions.