Deep learning compact binary codes for fingerprint indexing

Chao-chao BAI; Wei-qiang WANG; Tong ZHAO; Ru-xin WANG; Ming-qiang LI

doi:10.1631/FITEE.1700420

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Deep learning compact binary codes for fingerprint indexing

Regular Papers | Updated：2022-05-19

- Deep learning compact binary codes for fingerprint indexing
- 基于深度学习紧致二进制编码的指纹索引
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 9, Pages: 1112-1123(2018)
- Affiliations：
  
  School of Computer and Control, University of Chinese Academy of Sciences, Beijing 101408, China
  School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
  Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100080, China
- Author bio：
  
  Tong ZHAO, E-mail: zhaotong@ucas.ac.cn
- Funds：
- DOI：10.1631/FITEE.1700420
  CLC： TP311
- Received：26 June 2017，
  
  Revised：2018-;6-;7，
  
  Published Online：12 September 2018，
  
  Published：2018-09
- Accepted：
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Chao-chao BAI, Wei-qiang WANG, Tong ZHAO, et al. Deep learning compact binary codes for fingerprint indexing[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1112-1123.
DOI：

Chao-chao BAI, Wei-qiang WANG, Tong ZHAO, et al. Deep learning compact binary codes for fingerprint indexing[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1112-1123. DOI： 10.1631/FITEE.1700420.

摘要

随着指纹数据库迅速发展，有必要开发一种卓越的指纹索引方法满足系统高效性和准确性要求。实值特征的指纹索引已进行广泛研究，但二进制编码特征的研究相对较少，并且二进制编码特征更适合大规模指纹数据库的高效检索。首先，提出高效的有区分度的深度紧致二进制细节点圆柱体编码（deep compact binary minutia cylinder code，DCBMCC）作为指纹索引特征。具体分析了最新细节点圆柱体编码（minutia cylinder code，MCC），并发现其缺点。提出一种新颖的深度神经网络学习指纹索引特征DCBMCC，设置网络倒数第二层直接输出为二进制编码。将独立性、平衡性、量化损失最小和相似性保持等重要属性结合在学习过程中。最后，设计了基于多索引哈希（multi-index hashing，MIH）的指纹索引模式，从而在汉明空间中进行高效精确的搜索。此外，许多公开数据库上的实验表明，本文提出的方法是一个卓越的指纹索引方法，在穿透率非常低的情况下仍然具有非常小的错误率。

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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references

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