CLC number: TP311
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-06-07
Cited: 0
Clicked: 7851
Chao-chao Bai, Wei-qiang Wang, Tong Zhao, Ru-xin Wang, Ming-qiang Li. Deep learning compact binary codes for fingerprint indexing[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1112-1123.
@article{title="Deep learning compact binary codes for fingerprint indexing",
author="Chao-chao Bai, Wei-qiang Wang, Tong Zhao, Ru-xin Wang, Ming-qiang Li",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="9",
pages="1112-1123",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700420"
}
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%A Tong Zhao
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%J Frontiers of Information Technology & Electronic Engineering
%V 19
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700420
TY - JOUR
T1 - Deep learning compact binary codes for fingerprint indexing
A1 - Chao-chao Bai
A1 - Wei-qiang Wang
A1 - Tong Zhao
A1 - Ru-xin Wang
A1 - Ming-qiang Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 9
SP - 1112
EP - 1123
%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700420
Abstract: With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. fingerprint indexing has been widely studied with real-valued features, but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code (DCBMCC) as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code (MCC), as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed. Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC. Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process. Eventually, a multi-index hashing (MIH) based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.
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