Paper Info
Title
Fast exact fingerprint indexing based on Compact Binary Minutia Cylinder Codes.
Abstract
With explosive growth in fingerprint databases, Automatic Fingerprint Identification System has become more challenging than ever. Consequently, it is necessary to develop a fast and exact fingerprint indexing to meet the efficiency and accuracy. In this paper, learning Compact Binary Minutia Cylinder Code (CBMCC) is proposed as an effective and discriminative feature representation and Multi-Index Hashing (MIH) is suitably adopted to accelerate the exact search in fingerprint indexing field for the first time. Firstly, we analyze Minutia Cylinder Code to find that it is strongly bit-correlated and awfully unbalanced. Accordingly, we propose an optimization model to learn CBMCC with the balanced independent property and the minimal binary quantization loss. Finally, MIH method further speeds up the exact search in Hamming space by building multiple hash tables on binary code substrings. The performance test shows that CBMCC is effective and discriminative as it has the maximum intra-bit variance while the minimum inter-bit correlation. Furthermore, numerous experiments on public databases demonstrate that CBMCC-MIH is quite outstanding for fingerprint indexing since it achieves an extremely small error rate with a fairly low penetration rate. (c) 2017 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2018
10.1016/j.neucom.2017.10.027
NEUROCOMPUTING
Keywords
Field
DocType
Fingerprint indexing,Minutia Cylinder Code,Compact Binary Code,Multi-Index Hashing
Pattern recognition,Minutiae,Binary code,Fingerprint,Hash function,Artificial intelligence,Hamming space,Quantization (signal processing),Mathematics,Machine learning,Binary number,Hash table
Journal
Volume
ISSN
Citations 
275
0925-2312
1
PageRank 
References 
Authors
0.34
34
4
Name
Order
Citations
PageRank
Chaochao Bai182.08
Weiqiang Wang246949.23
Tong Zhao3147.30
Mingqiang Li4122.18