Kuo-Chun Lin
ABSTRACT
Over recent years, biometric systems have been popularly applied in various scenarios. Unfortunately, potential threats associated with these systems may lead to significant social concerns and pose risks to security. In this paper, an error-correction-based iris recognition (EC-IR) scheme which uses multi-scale dominating feature points (msDFPs) is produced to improve the security level against a stricter concern of error-correction-based attack. The EC-based attack can be initiated if an attacker is thoroughly knowledgeable about the structure of the adopted error correcting code. Hence, the system can be compromised with much fewer attempts compared to the brute force attack. To improve the number of security bits under this attack, the extraction method of the msDFPs is proposed to modify the essence of original iris data. As a result, it has been demonstrated that the proposed msDFPs-based EC-IR scheme provides a well-balanced design that ensures both a large value of security bits and an optimized recognition performance.
- [1] R. P. Wildes, “Iris recognition: An emerging biometric technology," Proceedings of the IEEE, vol. 85, no. 9, pp. 1348-1363, Sep. 1997.Google Scholar
Cross Ref
- [2] J. Daugman, “Statistical richness of visual phase information: update on recognizing persons by iris patterns," Int’l J. of Computer Vision, vol.45, no. 1,pp.25-38, 2001.Google ScholarDigital Library
- [3] J. Daugman, “How iris recognition works," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, Jan. 2004.Google ScholarDigital Library
- [4] K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding for iris biometrics: A survey," Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281-307, May 2008.Google ScholarDigital Library
- [5] Z. Sun and T. Tan, “Ordinal measures for iris recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2211-2226, Dec. 2009.Google Scholar
- [6] A. Kumar and A. Passi, “Comparison and combination of iris matchers for reliable personal authentication," Pattern recognition, vol. 43, no. 3, pp. 1016-1026, Mar. 2010.Google ScholarDigital Library
- [7] A. Ross, J. Shah, and A. K. Jain, “From template to image: Reconstructing fingerprints from minutiae points," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 544–560, Apr. 2007.Google ScholarDigital Library
- [8] A. Ceccarelli, L. Montecchi, F. Brancati, P. Lollini, A. Marguglio, and A. Bondavalli, “Continuous and transparent user identity verification for secure internet services," IEEE Transactions on Dependable and Secure Computing, vol. 12, no. 3, pp. 270-283, May 2015.Google ScholarDigital Library
- [9] J. Srinivas, A. K. Das, N. Kumar and J. J. P. C. Rodrigues, “Cloud Centric Authentication for Wearable Healthcare Monitoring System," IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 5, pp. 942-956, 1 Sept.-Oct. 2020.Google ScholarCross Ref
- [10] R. Dwivedi, S. Dey, “Securing fingerprint template using noninvertible ridge feature transformation," Journal of Electronic Imaging, 27(5) 053031, Oct. 2018.Google ScholarCross Ref
- [11] Y.-L. Lai et al., “Cancellable iris template generation based on indexing-first-one hashing," Pattern recognition, vol. 64, pp. 105–117, Apr. 2017.Google ScholarDigital Library
- [12] O. Ouda, N. Tsumura, and T. Nakaguchi, “Tokenless cancelable biometrics scheme for protecting iris codes," in 2010 20th International Conference on Pattern Recognition, Aug. 2010, pp. 882–885.Google Scholar
- [13] C. Rathgeb, F. Breitinger, and C. Busch, “Alignment-free cancelable iris biometric templates based on adaptive bloom filters,” 2013 International Conference on Biometrics (ICB), Jun. 2013, pp. 1–8.Google ScholarCross Ref
- [14] J. Zuo, N. K. Ratha, and J. H. Connell, “Cancelable iris biometric," 2008 19th International Conference on Pattern Recognition, Dec. 2008, pp. 1–4.Google Scholar
- [15] D. Sadhya and B. Raman, “Generation of Cancelable iris templates via randomized bit sampling," IEEE Transactions on Information Forensics and Security, vol. 14, no. 11, pp. 2972-2986, Nov. 2019.Google ScholarDigital Library
- [16] P. Punithavathi, S. Geetha, and S. Sasikala, “Generation of cancelable iris template using bi-level transformation," Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science, Jun. 2017, pp. 94–100.Google ScholarDigital Library
- [17] C. Rathgeb and C. Busch, “Cancelable multi-biometrics: mixing iris codes based on adaptive bloom filters," Computers & Security, vol. 42, pp. 1–12, 2014.Google ScholarCross Ref
- [18] M. Gomez-Barrero, C. Rathgeb, G. Li, R. Ramachandra, J. Galbally, and C. Busch, ‘Multi-biometric template protection based on bloom filters," Information Fusion, vol. 42, pp. 37–50, Jul. 2018.Google Scholar
- [19] C. Rathgeb and A. Uhl, “A survey on biometric cryptosystems and cancelable biometrics," EURASIP Journal on Information Security, vol. 2011, no. 1, p. 3, Sep. 2011. [Online]. Available: https://link.springer.com/article/10.1186/1687-417X-2011-3Google ScholarCross Ref
- [20] C. Li, J. Hu, J. Pieprzyk and W. Susilo, “A new biocryptosystem-oriented security analysis framework and implementation of multibiometric cryptosystems based on decision level fusion," IEEE Transactions on Information Forensics and Security, vol. 10, no. 6, pp. 1193-1206, June 2015.Google ScholarDigital Library
- [21] G. E. Hine, E. Maiorana and P. Campisi, “A zero-leakage fuzzy embedder from the theoretical formulation to real data," IEEE Transactions on Information Forensics and Security, vol. 12, no. 7, pp. 1724-1734, July 2017.Google ScholarDigital Library
- [22] Y.-L. Lai, J. Y. Hwang, Z. Jin, S. Kim, S. Cho and A. B. J. Teoh, “Secure secret sharing enabled b-band mini vaults bio-cryptosystem for vectorial biometrics," IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 1, pp. 58-71, 1 Jan.-Feb. 2021.Google ScholarDigital Library
- [23] U. Uludag, S. Pankanti, S. Prabhakar and A. K. Jain, “Biometric cryptosystems: issues and challenges," Proceedings of the IEEE, vol. 92, no. 6, pp. 948-960, June 2004.Google ScholarCross Ref
- [24] F. Hao, R. Anderson, and J. Daugman, “Combining crypto with biometrics effectively," IEEE Transactions on Computers, vol. 55, no. 9, pp.1081–1088, Sep. 2006.Google ScholarDigital Library
- [25] A. Vetro, S. Draper, S. Rane, J. S. Yedidia, “Securing biometric data," Distributed source coding, P. L. Dragotti and M. Gastpar, Eds. New York: Academic, 2009, ch. 11.Google Scholar
- [26] A. Juels, M. Wattenberg, “A fuzzy commitment scheme," in Proceedings of the 6th ACM conference on Computer and communications security, Singapore, 1999, pp. 28–36.Google Scholar
- [27] A. Juels and M. Sudan, “A fuzzy vault scheme," Designs, Codes and Cryptography, vol. 38, no. 2, pp. 237–257, Feb. 2006.Google ScholarDigital Library
- [28] W. Ponce-Hernandez, R. Blanco-Gonzalo, J. Liu-Jimenez and R. Sanchez-Reillo, “Fuzzy vault scheme based on fixed-length templates applied to dynamic signature verification," IEEE Access, vol. 8, pp. 11152-11164, 2020.Google ScholarCross Ref
- [29] S. Noto, P. L. Correia and L. D. Soares, “Analysis of error correcting codes for the secure storage of biometric templates," 2011 IEEE EUROCON - International Conference on Computer as a Tool, Lisbon, Portugal, 2011, pp. 1-4.Google Scholar
- [30] M. B. Ramalho, P. L. Correia, L. D. Soares, “Hand-based multimodal identification system with secure biometric template storage," IET Computer Vision, vol. 6, no. 3, pp. 165-173, May 2012.Google ScholarCross Ref
- [31] A. Stoianov, “Security of Error Correcting Code for biometric Encryption," 2010 Eighth International Conference on Privacy, Security and Trust, Ottawa, ON, Canada, Aug. 2010, pp.231-235.Google Scholar
- [32] T. M. Santos, L. D. Soares, and P. L. Correia, “Iris verification system with secure template storage," 010 18th European Signal Processing Conference, Aalborg, Denmark, 2010, pp. 279-283.Google Scholar
- [33] K.-C. Lin and Y.-M. Chen, “A high-security-level iris cryptosystem based on fuzzy commitment and soft reliability extraction," IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2023.3289916. (in early access)Google ScholarCross Ref
- [34] J. Galbally, A. Ross, M. Gomez-Barrero, J. Fierrez, and J. Ortega-Garcia, “From the iriscode to the iris: A new vulnerability of iris recognition systems," Black Hat Briefings USA, Jan., 2012Google Scholar
- [35] J. Galbally, A. Ross, M. Gomez-Barrero, J. Fierrez, and J. Ortega-Garcia, “Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms," Computer Vision and Image Understanding, 117(10):1512–1525, 2013.Google ScholarDigital Library
- [36] S. Ahmad and B. Fuller, “Resist: Reconstruction of irises from templates," 2020 IEEE International Joint Conference on Biometrics (IJCB), Houston, TX, USA, pp. 1-10, 2020.Google Scholar
- [37] S. Venugopalan and M. Savvides, “How to generate spoofed irises from an iris code template," IEEE Transactions on Information Forensics and Security, vol. 6, no. 2, pp. 385-395, June 2011.Google ScholarDigital Library
- [38] A. Kumar and A. Passi, “Comparison and combination of iris matchers for reliable personal authentication," Pattern Recognit., vol. 43, no. 3, pp. 1016-1026, 2010. [Online] Available: http ://www4.comp.polyu.edu.hk/c̃sajaykr/IITD/Database_Iris.htmlGoogle ScholarDigital Library
- [39] V. Rincon-Melchor, M. Nakano-Miyatake, O. Juarez-Sandoval, J. Olivares-Mercado, J. Moreno-Saenz and G. Benitez-Garcia, “Deep Learning Algorithm for the People Identification Using their ECG Signals as a Biometric Parameter," 2023 46th International Conference on Telecommunications and Signal Processing (TSP), Prague, Czech Republic, 2023, pp. 154-159.Google Scholar
- [40] D. G. Lowe, “Object recognition from local scaleinvariant features," Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 1999, pp. 1150-1157.Google Scholar
- [41] G. Evgin “Intensity normalization in brain MR images using spatially varying distribution matching," In: 11th International conference on computer graphics, visualization, computer vision and image processing (CGVCVIP), 2017, pp. 300–304.Google Scholar
- [42] E. Goceri, "Fully automated and adaptive intensity normalization using statistical features for brain MR images", Celal Bayar Üniversitesi Fen Bilimleri Dergisi., vol. 14, no. 1, 2018, pp. 125-134.Google ScholarCross Ref
- [43] Chinese Academy of Science Institute of Automation, CASIA-IrisV4-Distance. Accessed: Oct. 14, 2022. [Online] Available: http://biometrics.idealtest.org/dbDetailForUser.do?id=4Google Scholar
- [44] Chinese Academy of Science Institute of Automation, CASIA-IrisV4-Thousand. Accessed: Oct. 14, 2022. [Online] Available: http://biometrics.idealtest.org/dbDetailForUser.do?id=4Google Scholar
Index Terms
- Advanced Research on High-security-level Error-correction-based Iris Recognition System
Recommendations
Error Correction on IRIS Biometric Template Using Reed Solomon Codes
AMS '10: Proceedings of the 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer SimulationPin number or password that is used for authentication can be easily attacked. This limitation triggered the utilization of biometric for secured transactions. Biometric is unique to each individual and is reliable. Among the types of biometric being ...
Multimodal biometric system for ECG, ear and iris recognition based on local descriptors
AbstractCombination of multiple information extracted from different biometric modalities in multimodal biometric recognition system aims to solve the different drawbacks encountered in a unimodal biometric system. Fusion of many biometrics has proposed ...
Research on Personal Identity Verification Based on Hand Vein Iris and Fingerprint
ISCCS '11: Proceedings of the 2011 International Symposium on Computer Science and SocietyPersonal identity recognition is vital to information society. Multimodal biometric is proved to be a promising authentication technique. The fusion recognition algorithms were studied based on three biometric traits, hand vein, iris and fingerprint. ...
Comments