基于可编辑区块链的指纹识别系统

doi:10.3969/j.issn.0255-8297.2021.02.015

Abstract

Abstract: Fingerprint identity authentication system has been widely used in access control, payment, public security and other fields. Existing systems typically store original fingerprint images or features in a database to identify or authenticate users’ identity. Fingerprint data in the database is at risk of being attacked or tampered with. In order to solve this problem, this paper proposes a fingerprint identification system based on editable blockchain. Firstly, we build a private chain environment, achieve multi-node cluster interconnection, and then calculate the fingerprint hash and store it in the blockchain. In order to facilitate the administrator to update the users in the fingerprint identification system, this paper uses the chameleon hash algorithm to calculate the hash of the constructed private chain block. The administrator who owns the chameleon hash private key can edit the information in the block body to implement deletion or modification of the user fingerprint data without changing the blockchain structure. Experiments show that the proposed system has good real-time performance and high accuracy of fingerprint recognition.

Key words: editable blockchain, fingerprint recognition, fingerprint hash

CLC Number:

TP391

ZHU Yanyan, LI Sheng, FENG Guorui, ZHANG Xinpeng. Fingerprint Recognition System Based on Editable Blockchain[J]. Journal of Applied Sciences, 2021, 39(2): 330-337.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2021.02.015

https://www.jas.shu.edu.cn/EN/Y2021/V39/I2/330

References

[1] 新加坡网络空间安全战略[J]. 汕头大学学报(人文社会科学版), 2016, 32(6):157. Singapore's cybersecurity strategy[J]. Editorial Department of Shantou University Journal (Humanities and Social Sciences Edition), 2016, 32(6):157. (in Chinese)
[2] Nakamoto S. Bitcoin:a peer-to-peer electronic cash system[EB/OL].[2019-10-29]. https://bitcoin.org/en/bitcoin-paper.
[3] Zheng Z, Xie S, Dai H, et al. An overview of blockchain technology:architecture, consensus, and future trends[C]//2017 IEEE International Congress on Big Data (BigData Congress). IEEE, 2017:557-564.
[4] 周致成, 李立新, 郭松, 等. 基于区块链技术的生物特征和口令双因子跨域认证方案[J]. 计算机应用, 2018, 38(6):1620-1627. Zhou Z C, Li L X, Guo S, et al. Bi-factor crossdomain authentication scheme of biometrics and password based on blockchain technology[J]. Computer Application, 2018, 38(6):1620-1627. (in Chinese)
[5] 吕婧淑, 操晓春, 杨培. 基于区块链和人脸识别的双因子身份认证模型[J]. 应用科学学报, 2019, 37(2):164-178. Lü J S, Cao X C, Yang P. Two-factor identity authentication model based on blockchain ande face recognition[J]. Journal of Applied Sciences, 2019, 37(2):164-178. (in Chinese)
[6] Cappelli R, Ferrara M, Maltoni D. Fingerprint indexing based on minutia cylinder-code[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(5):1051-1057.
[7] Cappelli R, Ferrara M, Maltoni D. Minutia cylinder-code:a new representation and matching technique for fingerprint recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(12):2128-2141.
[8] Kim J, Teoh A B J. One-factor cancellable biometrics based on indexing-first-order hashing for fingerprint authentication[C]//International Conference on Pattern Recognition (ICPR). IEEE, 2018:3108-3113.
[9] Jin Z, Lim M H, Teoh A B J, et al. Generating fixed-length representation from minutiae using kernel methods for fingerprint authentication[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2016, 46(10):1415-1428.
[10] Jin Z, Hwang J Y, Lai Y L, et al. Ranking-based locality sensitive hashing-enabled cancelable biometrics:index-of-max hashing[J]. IEEE Transactions on Information Forensics and Security, 2017, 13(2):393-407.
[11] Lee M J, Jin Z, Teoh A B J. One-factor cancellable scheme for fingerprint template protection:extended feature vector (EFV) hashing[C]//2018 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2018:1-7.
[12] Kho J B, Teoh A B J, Lee W, et al. Fixed-length bit-string representation of fingerprint by normalized local structures[DB/OL]. 2018[2019-10-29]. https://arxiv.org/abs/1811.11489.
[13] Krawczyk H, Rabin T. Chameleon hashing and signatures:US6108783[P]. 2000-08-22[2019-10-29].
[14] Ateniese G, Magri B, Venturi D, et al. Redactable blockchain-or-rewriting history in bitcoin and friends[C]//2017 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, 2017:111-126.
[15] 李佩丽, 徐海霞, 马添军, 等. 可更改区块链技术研究[J]. 密码学报, 2018, 5(5):51-59. Li P L, Xu H X, Ma T J, et al. Research on changeable blockchain technology[J]. Journal of Cryptography, 2018, 5(5):51-59. (in Chinese)
[16] 任艳丽, 徐丹婷, 张新鹏, 等. 基于门限环签名的可删除区块链[J]. 通信学报, 2019, 40(4):71-82. Ren Y L, Xu D T, Zhang X P, et al. Deletable blockchain based on threshold ring signature[J]. Journal of Communication, 2019, 40(4):71-82. (in Chinese)

Fingerprint Recognition System Based on Editable Blockchain

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	LI Yongzhen, MA Fuyuan, MA Shixuan, WANG Yuhan, WANG Ying. Network Community Detection Based on Structure-Enhanced Deep Clustering [J]. Journal of Applied Sciences, 2026, 44(1): 1-20.
[2]	LI Yinxiang, DU Wenyuan, XU Zhe, PENG Chen, YAN Jianqiang. Real-Time Travel Pattern Recognition Algorithm Based on Self Adaptive Pooling Enhanced Attention Mechanism [J]. Journal of Applied Sciences, 2026, 44(1): 21-33.
[3]	ZHANG Yuting, TENG Fei, YE Xiaoqing. Uncertain Knowledge Graph Reasoning Model Based on Gaussian Metric Learning [J]. Journal of Applied Sciences, 2026, 44(1): 50-66.
[4]	XIANG Ga, HU Yan, ZHANG Yangsen, SUN Lu, QI Rui, TAN Zicheng. Construction of Malware Knowledge Graph for Threat Intelligence Analysis [J]. Journal of Applied Sciences, 2026, 44(1): 67-82.
[5]	YI Huawei, SONG Shixi, WANG Yanfei, BAI Siyi. Federated Recommendation Algorithm Integrating Graph Neural Networks and Depth Graph Clustering [J]. Journal of Applied Sciences, 2026, 44(1): 83-96.
[6]	WU Wenqiang, CHEN Aibin, LI Xiaoyao. Forest Image Dehazing Based on Feature Fusion Attention and Contrastive Learning [J]. Journal of Applied Sciences, 2026, 44(1): 97-109.
[7]	ZHANG Xiaoming, FENG Zejia, WANG Huiyong, ZHANG Xiaojing. Explainable Learning Path Recommendation Based on Dynamic Attention Reinforcement Learning [J]. Journal of Applied Sciences, 2026, 44(1): 110-133.
[8]	JIN Zhengyang, YAN Shaohong, ZHANG Yanbo, YAO Xulong, TAO Zhigang, CHEN Zhiyuan. Three-Dimensional Fuzzy Clustering Algorithm Integrating Spatial Texture Features [J]. Journal of Applied Sciences, 2026, 44(1): 134-148.
[9]	XU Kai, CHI Mingde, WANG Qi, LI Jianzhou, ZHANG Hui. Multi-granularity Semantic Aspect-Based Sentiment Analysis Model with Fusion of BERT Encoding Layers [J]. Journal of Applied Sciences, 2026, 44(1): 149-165.
[10]	LIU Yongchang, DU Yiying, WU Cuiying, LIU Yawen. Aerial Image-Guided LiDAR Point Cloud Semantic Segmentation [J]. Journal of Applied Sciences, 2025, 43(6): 922-934.
[11]	WANG Jinwei, HUANG Wanyun, ZHANG Jiawei, LUO Xiangyang, MA Bin. Active Defense Method Based on Recoverable Adversarial Watermarks [J]. Journal of Applied Sciences, 2025, 43(6): 935-947.
[12]	GUO Yanchun, XIONG Bangshu, LI Wenchao, WEN Shuyuan. Low-Light Image Enhancement of Helicopter Blades Based on Zero-Shot Network [J]. Journal of Applied Sciences, 2025, 43(6): 990-1002.
[13]	YAN Zhenguo, CHEN Yang, LIU Rufei, WANG Jinbo, ZHANG Jiaqi. Point Cloud Segmentation Method for Trees in Forest Sample Plots Based on Spatial Proximity [J]. Journal of Applied Sciences, 2025, 43(6): 1003-1014.
[14]	GAO Jianqi, HUANG Dian, LUO Xiangfeng. Entity-Event Relation Joint Extraction Enhanced with Syntactic Semantic [J]. Journal of Applied Sciences, 2025, 43(6): 1024-1036.
[15]	HAN Jiajie, YUAN Qingyang, ZHANG Bo, ZHAO Xin, LAN Tian, LI Yu. Improved Gappy POD Algorithm for Noisy Data Reconstruction Problems Based on Few Measurement Points [J]. Journal of Applied Sciences, 2025, 43(5): 740-756.