Digital fingerprint indexing using synthetic binary indexes

• Published in: Pattern analysis and applications : PAA Vol. 27; no. 2
• Main Authors: Falade, Joannes, Cremer, Sandra, Rosenberger, Christophe
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2024; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Computer Science; Cryptography and Security; Fingerprint verification; Identification; Indexing; Industrial and Commercial Application; Pattern Recognition; Minutiae; Local sensitive hashing function; Biometric indexing; Digital fingerprint; Binary representation; Biometrics; Identification
• DOI: 10.1007/s10044-024-01283-y
• ISSN: 1433-7541

Fingerprint identification is an important issue for people recognition when using Automatic Fingerprint Identification Systems (AFIS). The size of fingerprint databases has increased with the growing use of AFIS for identification at border control, visa issuance and other procedures around the world. Fingerprint indexing algorithms are used to reduce the fingerprint search space, speed up the identification processing time and also improve the accuracy of the identification result. In this paper, we propose a new binary fingerprint indexing method based on synthetic indexes to address this problem on large databases. Two fundamental properties are considered for these synthetic indexes: discriminancy and representativeness. A biometric database is then structured considering synthetic indexes for each fingerprint template, which guaranties to have a fixed number of indexes for the database during the enrollment and identification processes. We compare the proposed algorithm with the classical Minutiae Cylinder Code (MCC) indexing method, which is one of the best methods in the State of the art. In order to evaluate the proposed method, we use all Fingerprint Verification Competition (FVC) datasets from 2000 to 2006 databases separately and combined to confirm the accuracy of our algorithm for real applications. The proposed method achieves a high hit rate (more than 98%) for a low value of penetration rate (less than 5%) compared to existing methods in the literature.