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SP\(^2\)IN: Leveraging Fuzzy Commitment and LDPC Sum-Product Decoder for Key Generation from Face

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Biometric Recognition (CCBR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14463))

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Abstract

Biometric authentication, with its unique and convenient features, is gaining popularity as a secure access control method for diverse systems and services. However, the inherent ambiguity of biometric data poses challenges when integrating it with cryptographic systems that require 100% accuracy, such as personal information number generation. To address this, we propose SP\(^2\)IN, a face bio-cryptosystem that utilizes a Low-Density Parity-Check Sum-Product decoder and fuzzy commitment. In our innovative face biometric cryptosystem, we employ fuzzy commitment for secure key extraction from biometric input, ensuring the protection of sensitive facial information without compromising the privacy of the raw data. To tackle facial biometric noise, we use a Low-Density Parity-Check Sum-Product decoder for error correction against variations. Our system was rigorously tested on public face datasets (LFW, AgeDB, CALFW), showcasing outstanding recognition rates: 99.43% (LFW), 90.43% (CALFW), 92.63% (AgeDB30).

Y. Liu and Y. Zhou—These authors contributed equally to this work.

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Notes

  1. 1.

    Paired corresponds to intra-user, unpaired corresponds to inter-user, these terms are used interchangeably.

References

  1. Yan, J., Blackwell, A., Anderson, R., Grant, A.: Password memorability and security: empirical results. IEEE Secur. Priv. 2(5), 25–31 (2004)

    Article  Google Scholar 

  2. Spafford, E.H.: OPUS: preventing weak password choices. Comput. Secur. 11(3), 273–278 (1992)

    Article  Google Scholar 

  3. Sandhya, M., Prasad, M.V.N.K.: Biometric template protection: a systematic literature review of approaches and modalities. In: Jiang, R., Al-maadeed, S., Bouridane, A., Crookes, D., Beghdadi, A. (eds.) Biometric Security and Privacy. SPST, pp. 323–370. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47301-7_14

    Chapter  Google Scholar 

  4. EuropeanParliament, General Data Protection Regulation (GDPR). https://gdpr-info.eu/

  5. Lei, J., Pei, Q., Wang, Y., Sun, W., Liu, X.: PRIVFACE: fast privacy-preserving face authentication with revocable and reusable biometric credentials. IEEE Trans. Dependable Secur. Comput. 19, 3101–3112 (2021)

    Article  Google Scholar 

  6. Walia, G.S., Aggarwal, K., Singh, K., Singh, K.: Design and analysis of adaptive graph based cancelable multi-biometrics approach. IEEE Trans. Dependable Secur. Comput. 19, 54–66 (2020)

    Article  Google Scholar 

  7. Xi, K., Hu, J.: Bio-cryptography. In: Stavroulakis, P., Stamp, M. (eds.) Handbook of Information and Communication Security, pp. 129–157. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-04117-4_7

  8. Juels, A., Wattenberg, M.: A fuzzy commitment scheme. In: Proceedings of the 6th ACM Conference on Computer and Communications Security, ser. CCS 1999, ACM. New York, NY, USA, pp. 28–36. ACM (1999)

    Google Scholar 

  9. Hao, F., Anderson, R., Daugman, J.: Combining crypto with biometrics effectively. IEEE Trans. Comput. 55(9), 1081–1088 (2006)

    Article  Google Scholar 

  10. Kelkboom, E.J.C., Gökberk, B., Kevenaar, T.A.M., Akkermans, A.H.M., van der Veen, M.: “3D face’’: biometric template protection for 3D face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 566–573. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_60

    Chapter  Google Scholar 

  11. Li, P., Yang, X., Qiao, H., Cao, K., Liu, E., Tian, J.: An effective biometric cryptosystem combining fingerprints with error correction codes. Expert Syst. Appl. 39(7), 6562–6574 (2012)

    Article  Google Scholar 

  12. Rathgeb, C., Uhl, A.: A survey on biometric cryptosystems and cancelable biometrics. EURASIP J. Inf. Secur. 2011(1), 3 (2011)

    Article  Google Scholar 

  13. Bringer, J., Chabanne, H., Cohen, G., Kindarji, B., Zémor, G.: Optimal iris fuzzy sketches. In: 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE (2007)

    Google Scholar 

  14. Chen, B., Chandran, V.: Biometric based cryptographic key generation from faces. In: 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp. 394–401. IEEE (2007)

    Google Scholar 

  15. Yang, W., et al.: Securing mobile healthcare data: a smart card based cancelable finger-vein bio-cryptosystem. IEEE Access 6, 36939–36947 (2018)

    Google Scholar 

  16. Gilkalaye, B.P., Rattani, A., Derakhshani, R.: Euclidean-distance based fuzzy commitment scheme for biometric template security. In: 2019 7th International Workshop on Biometrics and Forensics (IWBF), pp. 1–6 (2019)

    Google Scholar 

  17. Dong, X., Jin, Z., Zhao, L., Guo, Z.: Bio can crypto: an LDPC coded bio-cryptosystem on fingerprint cancellable template. In: 2021 IEEE International Joint Conference on Biometrics, pp. 1–8. IEEE (2021)

    Google Scholar 

  18. Adamovic, S., Milosavljevic, M., Veinovic, M., Sarac, M., Jevremovic, A.: Fuzzy commitment scheme for generation of cryptographic keys based on iris biometrics. IET Biometrics 6(2), 89–96 (2017)

    Article  Google Scholar 

  19. Chen, L., Zhao, G., Zhou, J., Ho, A.T., Cheng, L.-M.: Face template protection using deep LDPC codes learning. IET Biometrics 8(3), 190–197 (2019)

    Article  Google Scholar 

  20. Phillips, P.J., et al.: Overview of the face recognition grand challenge. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1. pp, 947–954. IEEE (2005)

    Google Scholar 

  21. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  22. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Proceedings of fifth IEEE International Conference on Automatic Face Gesture Recognition, pp. 53–58. IEEE (2002)

    Google Scholar 

  23. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  24. Rathgeb, C., Merkle, J., Scholz, J., Tams, B., Nesterowicz, V.: Deep face fuzzy vault: implementation and performance. Comput. Secur. 113, 102539 (2022)

    Article  Google Scholar 

  25. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)

    Google Scholar 

  26. Multiplexing and Channel Coding, Release 16. 3GPP Standard TS 38.212, V16.0.0 (2019)

    Google Scholar 

  27. Drozdowski, P., Struck, F., Rathgeb, C., Busch, C.: Benchmarking binarisation schemes for deep face templates. In:2018 25th IEEE International Conference on Image Processing, pp. 191–195. IEEE (2018)

    Google Scholar 

  28. Lim, M.-H., Teoh, A.B.J.: A novel encoding scheme for effective biometric discretization: linearly separable subcode. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 300–313 (2012)

    Article  Google Scholar 

  29. Dang, T.K., Truong, Q.C., Le, T.T.B., Truong, H.: Cancellable fuzzy vault with periodic transformation for biometric template protection. IET Biometrics 5(3), 229–235 (2016)

    Article  Google Scholar 

  30. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  31. Deng, J., Guo, J., Zhang, D., Deng, Y., Lu, X., Shi, S.: Lightweight face recognition challenge. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)

    Google Scholar 

  32. Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in Real-Life Images: Detection, Alignment, and Recognition (2008)

    Google Scholar 

  33. Zheng, T., Deng, W., Hu, J.: Cross-age LFW: a database for studying cross-age face recognition in unconstrained environments. arXiv preprint arXiv:1708.08197 (2017)

  34. Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: the first manually collected, in-the-wild age database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 51–59 (2017)

    Google Scholar 

  35. Daugman, J.: Biometric decision landscapes. University of Cambridge, Computer Laboratory, Technical report (2000)

    Google Scholar 

  36. Boddeti, V.N.: Secure face matching using fully homomorphic encryption. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–10. IEEE (2018)

    Google Scholar 

  37. Dong, X., Kim, S., Jin, Z., Hwang, J.Y., Cho, S., Teoh, A.B.J.: A secure chaff-less fuzzy vault for face identification system. ACM Trans. Multimedia Comput. Commun. Appl. 17, 1–22 (2021)

    Article  Google Scholar 

  38. Zhang, K., Cui, H., Yu, Y.: Facial template protection via lattice-based fuzzy extractors. Cryptology ePrint Archive (2021)

    Google Scholar 

  39. Kumar Jindal, A., Chalamala, S., Kumar Jami, S.: Face template protection using deep convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 462–470 (2018)

    Google Scholar 

  40. Gomez-Barrero, M., Galbally, J., Rathgeb, C., Busch, C.: General framework to evaluate unlinkability in biometric template protection systems. IEEE Trans. Inf. Forensics Secur. 13(6), 1406–1420 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 62376003, 62306003) and the Anhui Provincial Natural Science Foundation (No. 2308085MF200).

Author information

Authors and Affiliations

  1. Anhui Provincial Key Laboratory of Secure Artificial Intelligence, Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Artificial Intelligence, Anhui University, Hefei, 230093, China

    Yafei Liu, Ying Zhou, Weiyu Zhou, Xingbo Dong & Zhe Jin

  2. Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Computer Science and Technology, Anhui University, Hefei, 230093, China

    Hui Zhang

  3. School of Medicine, Nankai University, Tianjin, 300071, China

    Yufang Dong

Authors
  1. Yafei Liu
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  2. Ying Zhou
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  3. Weiyu Zhou
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  4. Hui Zhang
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  5. Yufang Dong
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  6. Xingbo Dong
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  7. Zhe Jin
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Corresponding author

Correspondence to Zhe Jin .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Wei Jia

  2. South China University of Technology, Guangzhou, China

    Wenxiong Kang

  3. China University of Mining and Technology, Xuzhou, China

    Zaiyu Pan

  4. Shandong University, Jinan, China

    Xianye Ben

  5. China University of Mining and Technology, Xuzhou, China

    Zhengfu Bian

  6. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  7. Chinese Academy of Sciences, Beijing, China

    Zhaofeng He

  8. China University of Mining and Technology, Xuzhou, China

    Jun Wang

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Liu, Y. et al. (2023). SP\(^2\)IN: Leveraging Fuzzy Commitment and LDPC Sum-Product Decoder for Key Generation from Face. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_33

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  • DOI: https://doi.org/10.1007/978-981-99-8565-4_33

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  • Online ISBN: 978-981-99-8565-4

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