[1]王科俊,曹逸,邢向磊.基于MB-CSLBP的手指静脉加密算法研究[J].智能系统学报,2018,13(4):543-549.[doi:10.11992/tis.201704034]
WANG Kejun,CAO Yi,XING Xianglei.Finger-vein encryption algorithm based on MB-CSLBP[J].CAAI Transactions on Intelligent Systems,2018,13(4):543-549.[doi:10.11992/tis.201704034]
点击复制
WANG Kejun,CAO Yi,XING Xianglei.Finger-vein encryption algorithm based on MB-CSLBP[J].CAAI Transactions on Intelligent Systems,2018,13(4):543-549.[doi:10.11992/tis.201704034]
基于MB-CSLBP的手指静脉加密算法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第4期
页码:
543-549
栏目:
学术论文—机器学习
出版日期:
2018-07-05
- Title:
- Finger-vein encryption algorithm based on MB-CSLBP
- 关键词:
- 生物特征加密; 指静脉; 模糊承诺; MB-CSLBP编码; 模糊保险箱
- Keywords:
- biometric encryption; finger vein; fuzzy commitment; MB-CSLBP codes; fuzzy vault
- 分类号:
- TP391.41
- 摘要:
- 为解决在传统的生物特征加密技术的安全性上的不足，对手指静脉特征加密方法进行了探讨和研究。提出了基于MB-CSLBP编码的手指静脉加密方案。首先对LBP算子以及改进的CSLBP、MB-CSLBP算子进行了研究，提取了手指静脉的MB-CSLBP二进制特征编码。然后研究了传统的模糊承诺加密方案，在此基础上将提取的手指静脉MB-CSLBP二进制特征编码作为加密特征，对加密信息进行BCH编码后与加密特征以异或的方式结合完成加密，同时使用SHA-1散列算法对加密信息进行哈希变换，保留得到的哈希值以用于解密。实验结果表明，当密钥长度为400 b时，FAR达到了0.47%，文中提出的基于MB-CSLBP编码的手指静脉加密方案具有很高的鲁棒性和安全性。
- Abstract:
- In this paper, we investigate and discuss the biometric encryption of the finger vein to address the limitations of traditional biometric encryption. We propose a finger-vein encryption scheme based on multiscale block–center-symmetric local binary pattern (MB-CSLBP) binary coding. First, we investigate the LBP operator, the improved CSLBP, and the MB-CSLBP operator, and extract the MB-CSLBP binary code of the finger vein. Next, we investigate the traditional fuzzy commitment encryption scheme, and, with the extracted finger-vein MB-CSLBP binary codes as the encryption feature, we perform Bose, Chaudhuri, and Hocquenghem (BCH) encoding of the encryption information. Then, we combine the encryption information and encryption feature in an exclusive-OR manner, use the SHA-1 hash algorithm to perform a Hash transform, and keep the obtained Hash value for encryption. The experimental results show that the false acceptance rate reached 0.47% for a key length of 400 b. Thus, the finger-vein encryption method proposed in this paper demonstrates high robustness and security.
- 参考文献/References:
-
[1] DAUGMAN J G. High confidence visual recognition of persons by a test of statistical independence[J]. IEEE transactions on pattern analysis and machine intelligence, 1993, 15(11):1148-1161.
[2] AHADULLAH M, SAID M R M, BANERJEE S. History, development and trend of fractal based biometric cryptography[M]//ER?ETIN?S S, BANERJEE S. Chaos, Complexity and Leadership 2013. Cham:Springer, 2015:27-33.
[3] NAVEEN K H N, JAGADEESHA S, AMITH K J. Human facial expression recognition from static images using shape and appearance feature[C]//Proceedings of the 2nd International Conference on Applied and Theoretical Computing and Communication Technology. Bangalore, India, 2016:598-603.
[4] HUANG Di, ZHANG Renke, YIN Yuan, et al. Local feature approach to dorsal hand vein recognition by centroid-based circular key-point grid and fine-grained matching[J]. Image and vision computing, 2017, 58:266-277.
[5] RATHA N K, CONNELL J H, BOLLE R M. An analysis of minutiae matching strength[C]//Proceedings of the 3rd International Conference on Audio- and Video-Based Biometric Person Authentication. Halmstad, Sweden, 2001:223-228.
[6] JAIN A K, NANDAKUMAR K, NAGAR A. Biometric template security[J]. EURASIP journal on advances in signal processing, 2008, 2008:279416.
[7] 张宁, 臧亚丽, 田捷. 生物特征与密码技术的融合——一种新的安全身份认证方案[J]. 密码学报, 2015, 2(2):159-176. ZHANG Ning, ZANG Yali, TIAN Jie. The integration of biometrics and cryptography-a new solution for secure identity authentication[J]. Journal of cryptologic reseatch, 2015, 2(2):159-176.
[8] LI Peng, YANG Xin, CAO Kai, et al. An alignment-free fingerprint cryptosystem based on fuzzy vault scheme[J]. Journal of network and computer applications, 2010, 33(3):207-220.
[9] CHUNG Y, MOON D, LEE S, et al. Automatic alignment of fingerprint features for fuzzy fingerprint vault[C]//Proceedings of the 5st SKLOIS Conference Information Security and Cryptology. Beijing, China, 2005:358-369.
[10] ULUDAG U, PANKANTI S, PRABHAKAR S, et al. Biometric cryptosystems:issues and challenges[J]. Proceedings of the IEEE, 2004, 92(6):948-960.
[11] PERALTA D, TRIGUERO I, SANCHEZ-REILLO R, et al. Fast fingerprint identification for large databases[J]. Pattern recognition, 2014, 47(2):588-602.
[12] CHAUDHARY D R, SHARMA A. Hand geometry based recognition system[C]//Proceedings of 2012 Nirma University International Conference on Engineering. Ahmedabad, India, 2012:1-5.
[13] JING Xiaoyuan, LI Sheng, ZHANG D, et al. Optimal subset-division based discrimination and its kernelization for face and palmprint recognition[J]. Pattern recognition, 2012, 45(10):3590-3602.
[14] 王科俊, 曹逸, 姜博威, 等. 基于纠错码的指静脉加密算法[J]. 智能系统学报, 2017, 12(1):55-59. WANG Kejun, CAO Yi, JIANG Bowei, et al. Finger vain encryption algorithm based on an error-correcting code[J]. CAAI transactions on intelligent systems, 2017, 12(1):55-59.
[15] YOU Lin, WANG Jiawan, YAN Bin. A secure finger vein recognition algorithm based on MB-GLBP and Logistic mapping[J]. Journal of information hiding and multimedia signal processing, 2016, 7(6):1231-1242.
[16] ZHANG D, ZUO Wangmeng, YUE Feng. A comparative study of palmprint recognition algorithms[J]. ACM computing surveys (CSUR), 2012, 44(1):2.
[17] OJALA T, PIETIK?INEN M, HARWOOD D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern recognition, 1996, 29(1):51-59.
[18] OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(7):971-987.
[19] AHONEN T, PIETIK?INEN M. A framework for analyzing texture descriptors[C]//Proceedings of the 3rd International Conference on Computer Vision Theory and Applications. Madeira, Portugal, 2008:1.
[20] HEIKKILA M, PIETIKAINEN M. A texture-based method for modeling the background and detecting moving objects[J]. IEEE transactions on pattern analysis and machine intelligence, 2006, 28(4):657-662.
[21] HEIKKIL? M, PIETIK?INEN M, SCHMID C. Description of interest regions with local binary patterns[J]. Pattern recognition, 2009, 42(3):425-436.
[22] MALOFEY O P, MALOFEY A O, SHANGINA A E. Enhancing the functionality of the procedures of encoding and decoding BCH codes[C]//Proceedings of 2017 International Conference "Quality Management, Transport and Information Security, Information Technologies". Petersburg, Russia, 2017:243-246.
[23] SLIMANE N B, BOUALLEGUE K, MACHHOUT M. A novel image encryption scheme using chaos, hyper-chaos systems and the secure Hash algorithm SHA-1[C]//Proceedings of 2017 International Conference on Control, Automation and Diagnosis. Hammamet, Tunisia, 2017:141-145.
[24] SLIMANE N B, BOUALLEGUE K, MACHHOUT M. Nested chaotic image encryption scheme using two-diffusion process and the Secure Hash Algorithm SHA-1[C]//Proceedings of the 4th International Conference on Control Engineering and Information Technology. Hammamet, Tunisia, 2016:1-5.
[25] MAKKAD R K, SAHU A K. Novel design of fast and compact SHA-1 algorithm for security applications[C]//Proceedings of IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology. Bangalore, India, 2016:921-925.
备注/Memo
收稿日期:2017-04-24。
基金项目:国家自然科学基金面上项目（61573114）；黑龙江省自然科学基金面上项目（F2015033）；中央高校基本科研基金项目（HEUCF160415）.
作者简介:王科俊,男,1962年生,教授,博士生导师,主要研究方向为模糊混沌神经网络、自适应逆控制理论、可拓控制、网络智能控制、模式识别、多模态生物特征识别、联脱机指纹考试身份鉴别系统、微小型机器人系统;曹逸,女,1993年生,硕士研究生,主要研究方向为模式识别和生物特征识别;邢向磊,男,1983年生,讲师,博士后,主要研究方向为多集合度量学习和远距离身份识别。
通讯作者:邢向磊.E-mail:xingxl@hrbeu.edu.cn.
更新日期/Last Update:
2018-08-25