Abstract
Palmprint recognition is a promising biometric technology. Currently, the research on palmprint recognition focuses on the topic of feature extraction and matching. However, due to the characteristic that biometric traits cannot be modified at will, how to secure and protect the privacy of palmprint recognition is a neglected and valuable topic. In this paper, we propose a privacy-preserving framework for palmprint recognition based on homomorphic encryption. Specially, for any given eligible palmprint recognition network, it is encrypted layer by layer to obtain both image key and network key. Particularly, the introduced homomorphic encryption strategy does not cause any loss of recognition accuracy, which ensures the wide applicability of the proposed method. In addition, it greatly reduces the risk of exposing plaintext images and model parameters, thus circumventing potential attacks against data and models. Adequate experiments on constrained and unconstrained palmprint databases verify the effectiveness of our method.
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References
Liu, W., Kou, K.I., Miao, J., Cai, Z.: Quaternion scalar and vector norm decomposition: quaternion PCA for color face recognition. IEEE Trans. Image Process. 32, 446–457 (2023)
Zhu, Y., Yin, X., Hu, J.: FingerGAN: a constrained fingerprint generation scheme for latent fingerprint enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 45(7), 8358–8371 (2023)
Shao, H., Zhong, D.: Multi-target cross-dataset palmprint recognition via distilling from multi-teacher. IEEE Trans. Instrum. Meas. 72, 1–14 (2023)
Iezzi, M.: Practical privacy-preserving data science with homomorphic encryption: an overview. In: 8th IEEE International Conference on Big Data, pp. 3979–3988. IEEE, Atlanta (2020)
Kim, D., Guyot, C.: Optimized privacy-preserving CNN inference with fully homomorphic encryption. IEEE Trans. Inf. Forensics Secur. 18, 2175–2187 (2023)
Bugshan, N., Khalil, I., Rahman, M.S., Atiquzzaman, M., Yi, X., Badsha, S.: Toward trustworthy and privacy-preserving federated deep learning service framework for industrial internet of things. IEEE Trans. Ind. Inform. 19(2), 1535–1547 (2023)
Zhong, D., Du, X., Zhong, K.: Decade progress of palmprint recognition: a brief survey. Neurocomputing 328, 16–28 (2019)
Wu, X., Zhang, D., Wang, K.: Palm line extraction and matching for personal authentication. IEEE Trans. Syst. Man Cybern. Part A 36(5), 978–987 (2006)
Zhong, D., Shao, H., Du, X.: A hand-based multi-biometrics via deep hashing network and biometric graph matching. IEEE Trans. Inf. Forensics Secur. 14(12), 3140–3150 (2019)
Xiang, C., Tang, C., Cai, Y., Xu, Q.: Privacy-preserving face recognition with outsourced computation. Soft. Comput. 20(9), 3735–3744 (2016)
Sun, D., et al.: Face security authentication system based on deep learning and homomorphic encryption. Secur. Commun. Netw. 2022, 1–8 (2022)
Byrne, J., DeCann, B., Bloom, S.: Key-nets: optical transformation convolutional networks for privacy preserving vision sensors. In: 31st British Machine Vision Conference, pp. 1–26. Springer, UK (2020). https://doi.org/10.48550/arXiv.2008.04469
Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84–92. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24261-3_7
Deng, J., Guo, J., Yang, J., Xue, N., Kotsia, I., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 5962–5979 (2022)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62206218 and Grant 62376211, in part by Zhejiang Provincial Natural Science Foundation of China under Grant LTGG23F030006, in part by Young Talent Fund of Association for Science and Technology in Shaanxi, China, under Grant XXJS202231, in part by the Xi’an Science and Technology Project under Grant 23ZCKCGZH0001, and in part by Fundamental Research Funds for the Central Universities under Grant xzy012023061.
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Guo, Q., Shao, H., Liu, C., Wan, J., Zhong, D. (2023). Homomorphic Encryption-Based Privacy Protection for Palmprint Recognition. 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_34
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DOI: https://doi.org/10.1007/978-981-99-8565-4_34
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