Towards Bloom Filter-Based Indexing of Iris Biometric Data
Iris (Eye), Computational complexity, Databases, Biometric identification
Computer Engineering | Computer Sciences | Electrical and Computer Engineering | Forensic Science and Technology | Information Security
Conventional biometric identification systems require exhaustive 1 : N comparisons in order to identify biometric probes, i.e. comparison time frequently dominates the overall computational workload. Biometric database indexing represents a challenging task since biometric data is fuzzy and does not exhibit any natural sorting order. In this paper we present a preliminary study on the feasibility of applying Bloom filters for the purpose of iris biometric database indexing. It is shown, that by constructing a binary tree data structure of Bloom filters extracted from binary iris biometric templates (iris-codes) the search space can be reduced to O(logN). In experiments, which are carried out on a database of N = 256 classes, biometric performance (accuracy) is maintained for different conventional identification systems. Further, perspectives on how to employ the proposed scheme on large-scale databases are given.
Rathgeb, Christian; Breitinger, Frank; Baier, Harald; and Busch, Christer, "Towards Bloom Filter-Based Indexing of Iris Biometric Data" (2015). Electrical & Computer Engineering and Computer Science Faculty Publications. 39.
Rathgeb, C., Breitinger F., Baier, H., Busch, C. "Towards Bloom filter-based indexing of iris biometric data." In Biometrics (ICB), 2015 International Conference on. New York: IEEE, Pp. 422-429.