Blockchain's core features are “decentralized” and “non-real-name,” so blockchain technology is well suited for authentication scenarios. Firstly, this paper expounds the history and principle of identity authentication and blockchain development. Secondly, it points out the security problems of traditional identity authentication mechanism due to centralized storage. On this basis, it also put forward two-factor identity authentication model based on blockchain and face recognition, defnes and describes the participants and components of the model, details the specifc processes of each operation involved in the model. Finally, the security of the model is proved by simulating attack and resistance analysis, and the availability of the model is proved by efciency and storage analysis.
LÜ Jing-shu, CAO Xiao-chun, YANG Pei
. Two-Factor Identity Authentication Model Based on Blockchain and Face Recognition[J]. Journal of Applied Sciences, 2019
, 37(2)
: 164
-178
.
DOI: 10.3969/j.issn.0255-8297.2019.02.002
[1] O'Gorman L. Comparing passwords, tokens, and biometrics for user authentication[C]//Proceedings of the IEEE, 2003:2019-2020.
[2] Marwick A E. Scandal or sex crime? Gendered privacy and the celebrity nude photo leaks[J]. Ethics and Information Technology, 2017:177-191.
[3] Jain A, Hong L, Pankanti S. Biometric identifcation[J]. Communications of the ACM, 2000, 43(2):90-98.
[4] Zheng Z, Xie S, Dai H N, Chen X, Wang H. Blockchain challenges and opportunities:a survey[J]. International Journal of Web and Grid Services, 2018, 14:352-375.
[5] Degui Z, Geng Y. Content distribution mechanism in mobile P2P network[J]. Journal of Networks, 2014, 9(5):1229.
[6] Zyskind G, Nathan O, Pentland A. Decentralizing privacy:using blockchain to protect personal data[C]//Security and Privacy Workshops. IEEE, 2015:180-184.
[7] Zheng Z, Xie S, Dai H, Chen X, Wang H. An overview of blockchain technology:architecture, consensus, and future trends[C]//International Congress on Big Data (BigData Congress). IEEE, 2017:557-564.
[8] Hankerson D, Vanstone S, Menezes A. Guide to elliptic curve cryptography[M]. New York:Springer Professional Computing, 2004.
[9] Gilbert H, Handschuh H. Security analysis of SHA-256 and sisters[C]//International Workshop on Selected Areas in Cryptography. Berlin, Heidelberg:Springer, 2003:175-193.
[10] Dobbertin H, Bosselaers A, Preneel B. RIPEMD-160:a strengthened version of RIPEMD[C]//International Workshop on Fast Software Encryption. Berlin, Heidelberg:Springer, 1996:71-82.
[11] Cachin C. Architecture of the hyperledger blockchain fabric[C]//Workshop on Distributed Cryptocurrencies and Consensus Ledgers. 2016:310.
[12] Dunphy P, Petitcolas F. A frst look at identity management schemes on the blockchain[C]//IEEE Security & Privacy, 2018, 16:20-29.
[13] Parkhi O M, Vedaldi A, Zisserman A. Deep face recognition[C]//British Machin Vision Conference, 2015, 1(3):6.
[14] Lamdan Y, Wolfson H J. Geometric hashing:a general and efcient model-based recognition scheme[C]//Second International Conference on Computer Vision, 1988:238-249.
[15] Zhang, J, Yong Y, Martin L.Face recognition:eigenface, elastic matching, and neural nets[C]//Proceedings of the IEEE, 1997, 85(9):1423-1435.
[16] Joachims T. Making large-scale SVM learning practical, advances in kernel methods:support vector learning[M]. Cambridge:MIT Press, 1999.
[17] Taigman Y, Yang M, Ranzato M, Wolf L. Deepface:closing the gap to human-level performance in face verifcation[C]//Conference on Computer Vision and Pattern Recognition. IEEE, 2014:1701-1708.
[18] Ouyang W, Wang X, Zeng X, Qiu S, Luo P, Tian Y, Li H, Yang S, Wang Z, Loy C C, T X. Deepid-net:deformable deep convolutional neural networks for object detection[C]//Conference on Computer Vision and Pattern Recognition. IEEE, 2015:2403-2412.
[19] Schroff F, Kalenichenko D, Philbin J. Facenet:a unifed embedding for face recognition and clustering[C]//Conference on Computer Vision and Pattern Recognition. IEEE, 2015:815-823.
[20] Liu W, Wen Y, Yu Z, Li M, Raj B, Song L. Sphereface:deep hypersphere embedding for face recognition[C]//Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017:212-220.
[21] Halfond W G, Jeremy V, Alessandro O. A classifcation of SQL-injection attacks and countermeasures[C]//International Symposium on Secure Software Engineering. IEEE, 2006, 1:13-15.
[22] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition.[C]//Conference on Computer Vision and Pattern Recognition. IEEE, 2017:770-778.
[23] Yi D, Lei Z, Liao S, Li S Z. Learning face representation from scratch[J]. Computer Science, 2014:abs/1411.7923.
[24] Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R. Caffe:convolutional architecture for fast feature embedding[C]//International Conference on Multimedia. ACM, 2014:675-678.
[25] Zhang K, Zhang Z, Li Z, Qiao Y. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 2016, 23(10):1499-1503.
[26] Szydlo M. Merkle tree traversal in log space and time[C]//Eurocrypt, 2004, 3027:541-554.
[27] Biham E, Shamir A. Differential cryptanalysis of the data encryption standard[M]. Springer Science & Business Media, 2012.
