Yen Lung Lai
ABSTRACT
Eye iris has been widely recognized as one of the strongest biometrics attributed to its high accuracy performance. However, any compromised event of iris data potentially leads to severe security and privacy issues because the human iris is permanently linked to individuals and not revocable. Excising protection schemes protect the iris data with the expense of decreased accuracy performance. This paper introduces a new protection scheme to generate a protected template from iris data that can be safely store in the database for future authentication. Experiment results showed that the proposed scheme enjoys a particular S-curve property required to offer strong system security while ensuring high system usability in terms of low false acceptance and false rejection rate.
- S. Nanavati, M.Thieme and R. Nanavati, Biometrics: IdentityVerification in a Networked World, Wiley, New York, 2002.Google Scholar
- J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, 2004.Google ScholarDigital Library
- J. Daugman, “Probing the uniqueness and randomness of iris codes: results from 200 billion iris pair comparisons,” in Proceedings of the IEEE, 2006.Google Scholar
- J. Daugman, “Recognising persons by their iris patterns,” in Advances in Biometric Person Authentication, ser. Lecture Notes in Computer Science, S. Li, J. Lai, T. Tan, G. Feng, and Y. Wang, Eds., Springer Berlin / Heidelberg, 2005, vol. 3338, pp. 783–814., 2005.Google Scholar
- Chen, Y., Wo, Y., Xie, R., Wu, C., & Han, G, “Deep Secure Quantization: On secure biometric hashing against similarity-based attacks,” Signal Process., vol. 154, pp. 314-323, 2019.Google ScholarCross Ref
- Y. Lai, Z. Jin, K. Wong and M. Tistarelli, “Efficient Known-Sample Attack for Distance-Preserving Hashing Biometric Template Protection Schemes,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3170-3185, 2021.Google ScholarCross Ref
- J. Daugman, Score Normalization Rules in Iris Recognition, Boston: In: Li S.Z., Jain A. (eds) Encyclopedia of Biometrics. Springer, MA. https://doi.org/10.1007/978-0-387-73003-5_169, 2009.Google ScholarCross Ref
- J. Galbally, A. Ross, M. Gomez-Barrero, J. Fi´errez, and J. Ortega-Garcia, “Iris image reconstruction from binary templates: An efficient probabilistic approach,” Computer Vision and Image Understanding, vol. 117, no. 10, p. 1512–1525, 2013.Google ScholarDigital Library
- 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, p. 385–395, 2011.Google ScholarDigital Library
- I. J. S. S. Techniques, “ISO/IEC 24745:2011,” Information Technology - Security Techniques - Biometric Information Protection, International Organization for Standardization, 2011., :2011.Google Scholar
- S.C. Chong, A.T.B. Jin and D.N.C. Ling, “High security iris verification system based on random secret integration,” Computer Vision and Image Understanding, vol. 102, no. 2, pp. 169-177, 2006.Google ScholarCross Ref
- Andrew Teoh Beng Jin, David Ngo Chek Ling, Alwyn Goh,, “Biohashing: two factor authentication featuring fingerprint data and tokenised random number,” Pattern Recognition, vol. 3, no. 11, pp. Pages 2245-2255, 2004.Google Scholar
- J. Zuo, N.K. Ratha and J.H. Connel, “Cancelable iris biometric,” in Proceedings of 19th International Conference on Pattern Recognition (ICPR 2008), 2008.Google Scholar
- J.K. Pillai, V.M. Patel, R. Chellappa and N.K. Ratha, “Sectored random projections for cancelable iris biometrics,” in Proceeding IEEE International Conference on Acoustics Speech and Signal Processing, 2010.Google ScholarCross Ref
- J. Hämmerle -Uhl, E. Pschernig and A. Uhl, “Cancelable iris biometrics using block re–mapping and image warping,” 2009.Google Scholar
- O. Ouda, N. Tsumura and T. Nakaguchi, “On the Security of BioEncoding Based Cancelable Biometrics,” On the Security of BioEncoding Based Cancelable Biometrics. IEICE Transaction on Information and Systems, Vols. E94-D, no. 9, pp. 1768-1777, 2011.Google Scholar
- O. Ouda, N. Tsumura and T. Nakaguchi, “Tokenless cancelable biometrics scheme for protecting iris codes,” in Proceedings of 20th International Conference on Pattern Recognition (ICPR10), 2010.Google ScholarDigital Library
- R. Dwivedi and D. Somnath, “Cancelable iris template generation using look-up table mapping,” in Signal Processing and Integrated Networks (SPIN), 2015.Google ScholarCross Ref
- C. Rathgeb, F. Breitinger and C. Busch, “Alignment-free cancelable iris biometric templates based on adaptive bloom filters,” in In IEEE International Conference on Biometrics, 2013.Google ScholarCross Ref
- Yen-Lung Lai, Zhe Jin, Andrew Beng Jin Teoh, Bok-Min Goi, Wun-She Yap, Tong-Yuen Chai, Christian Rathgeb,, “Cancellable iris template generation based on Indexing-First-One hashing,” Pattern Recognition, vol. 64, pp. 105-117, 2017.Google ScholarDigital Library
- Sadhya, D.; Raman, B., “Generation of cancelable iris templates via randomized bit sampling.,” IEEE Transactions on Information Forensics and Security 2019, 14, 2972-2986., vol. 14, pp. 2972-2986., 2019, 14, 2972-2986..Google Scholar
- Rajaraman, A., and Ullman, J. D. , Mining of massive datasets, Cambridge University Press., 2011.Google Scholar
- A. Kong, K.H. Cheung, D. Zhang, M. Kamel and J. You, “An analysis of BioHashing and its variants,” Pattern Recognition, vol. 39, no. 7, pp. 1359-1368, 2006.Google ScholarDigital Library
- P. Lacharme, E. Cherrier, and C. Rosenberger, “Reconstruction attack on BioHashing,” in International Conference on Security and Cryptography (SECRYPT), 2013.Google Scholar
- S. Jenisch and A. Uhl, “Security analysis of a cancelable iris recognition system based on block remapping,” in Proceedings of IEEE International Conference on Image Processing, 2011.Google ScholarCross Ref
- P. Lacharme, “Analysis of the iriscodes bioencoding scheme,” International Journal of Computer Science and Software Engineering (IJCSSE 2012), vol. 6, no. 5, pp. 315-321, 2012.Google Scholar
- J. Hermans, B. Mennink and R. Peeters, “When a Bloom filter is a Doom filter: Security assessment of a novel iris biometric template protection system,” in Proceedings of the Biometrics Special Interest Group. Lecture Notes in Informatics (LNI), 2014.Google Scholar
- J. Bringer, C. Morel and C. Rathgeb, “Security analysis of Bloom filter-based iris biometric template protection,” in 2015 International Conference on Biometrics (ICB), 2015.Google ScholarCross Ref
Recommendations
Cross-Sensor Iris Recognition through Kernel Learning
Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, ...
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 ...
The Biometric Menagerie
It is commonly accepted that users of a biometric system may have differing degrees of accuracy within the system. Some people may have trouble authenticating, while others may be particularly vulnerable to impersonation. Goats, wolves, and lambs are ...
Comments