Small-size fingerprint matching based on deep learning

doi:10.11772/j.issn.1001-9081.2017.11.3212

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (11): 3212-3218.DOI: 10.11772/j.issn.1001-9081.2017.11.3212

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Small-size fingerprint matching based on deep learning

ZHANG Yongliang¹, ZHOU Bing¹, ZHAN Xiaosi², QIU Xiaoguang³, LU Tianpei¹

1. School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou Zhejiang 310023, China;
2. School of Computer Science and Technology, Zhejiang International Studies University, Hangzhou Zhejiang 310024, China;
3. Hangzhou Commnet Company Limited, Hangzhou Zhejiang 310012, China

Received:2017-05-11 Revised:2017-06-05 Online:2017-11-10 Published:2017-11-11
Supported by:
This work is partially supported by the Natural Science Foundation of Zhejiang Province (Y1101304).

基于深度学习的小面积指纹匹配方法

张永良¹, 周冰¹, 詹小四², 裘晓光³, 卢天培¹

1. 浙江工业大学计算机科学与技术学院, 杭州 310023;
2. 浙江外国语学院计算机科学与技术学院, 杭州 310024;
3. 杭州易和网络有限公司, 杭州 310012

通讯作者: 张永良
作者简介:张永良(1977-),男,浙江杭州人,副教授,博士,CCF会员,主要研究方向:生物特征识别、模式识别、人工智能、机器学习;周冰(1991-),男,浙江宁波人,硕士研究生,主要研究方向:生物特征识别、机器学习;詹小四(1975-),男,安徽桐城人,教授,博士,主要研究方向:图像处理、模式识别、生物特征识别、机器学习;裘晓光(1976-),男,浙江杭州人,高级工程师,硕士,主要研究方向:生物特征识别、智能指纹锁;卢天培(1996-),男,浙江杭州人,主要研究方向:生物特征识别、模式识别。
基金资助:
浙江省自然科学基金资助项目（Y1101304）。

Abstract

Abstract: Focused on the issue that the traditional fingerprint matching methods based on minutiae are mainly applicable for large-size fingerprint and the accuracy rate would reduce significantly when dealing with small-size fingerprint from smart phone, a small-size fingerprint matching method based on deep learning was proposed. Firstly, the detailed information of minutiae was extracted from fingerprint images. Secondly, the Regions Of Interest (ROI) were searched and labeled based on minutiae. Then a lightweight deep neural network was built and improved from original residual module. In addition, binary feature pattern and triplet loss were used to optimize and train the proposed model respectively. Finally, the small-size fingerprint matching was accomplished with the fusion strategy of registration and matching. The experimental results show that the Equal Error Rate (EER) of the proposed method can reach 0.50% and 0.58% on public FVC_DB1 and in-house database respectively, which is much lower than the traditional fingerprint matching methods based on minutiae, and can improve the performance of small-size fingerprint matching effectively and meet the requirements on smart phone.

Key words: fingerprint matching, deep learning, Convolutional Neural Network (CNN), triplet loss

摘要： 针对传统的基于细节特征点的指纹匹配方法多适用于采集面积较大的指纹，在面向智能手机端的小采集面积指纹时准确率明显下降的问题，提出一种基于深度学习的小面积指纹匹配方法。首先，提取指纹图像的细节特征点信息；其次，搜索和标定感兴趣纹理区域（ROI）；然后，构建并改进基于残差结构的轻量级深度神经网络，通过采用二值化特征模式优化网络和Triplet Loss方式训练模型；最后，制定一种智能手机端注册-匹配策略实现小面积指纹匹配。实验结果表明，提出方法在公开库FVCDB1与自建数据库上的等错率（EER）分别仅为0.50%与0.58%，远低于传统的基于细节特征点的指纹匹配方法，能够有效提升小面积指纹匹配的性能，更好地满足智能手机端的应用需求。

关键词: 指纹匹配, 深度学习, 卷积神经网络, Triplet Loss

CLC Number:

TP183

ZHANG Yongliang, ZHOU Bing, ZHAN Xiaosi, QIU Xiaoguang, LU Tianpei. Small-size fingerprint matching based on deep learning[J]. Journal of Computer Applications, 2017, 37(11): 3212-3218.

张永良, 周冰, 詹小四, 裘晓光, 卢天培. 基于深度学习的小面积指纹匹配方法[J]. 计算机应用, 2017, 37(11): 3212-3218.

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Small-size fingerprint matching based on deep learning

基于深度学习的小面积指纹匹配方法

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