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Rethinking Contemporary Deep Learning Techniques for Error Correction in Biometric Data

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Abstract

In the realm of cryptography, the implementation of error correction in biometric data offers many benefits, including secure data storage and key derivation. Deep learning-based decoders have emerged as a catalyst for improved error correction when decoding noisy biometric data. Although these decoders exhibit competence in approximating precise solutions, we expose the potential inadequacy of their security assurances through a minimum entropy analysis. This limitation curtails their applicability in secure biometric contexts, as the inherent complexities of their non-linear neural network architectures pose challenges in modeling the solution distribution precisely. To address this limitation, we introduce U-Sketch, a universal approach for error correction in biometrics, which converts arbitrary input random biometric source distributions into independent and identically distributed (i.i.d.) data while maintaining the pairwise distance of the data post-transformation. This method ensures interpretability within the decoder, facilitating transparent entropy analysis and a substantiated security claim. Moreover, U-Sketch employs Maximum Likelihood Decoding, which provides optimal error tolerance and a precise security guarantee.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 62376003, 62076086, 62476077) and Anhui Provincial Natural Science Foundation (No. 2308085MF200)

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Authors and Affiliations

  1. Anhui Provincial Key Laboratory of Secure Artificial Intelligence School of Artificial Intelligence, Anhui University, Hefei, 230093, China

    YenLung Lai, XingBo Dong & Zhe Jin

  2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Wei Jia

  3. Computer Vision Laboratory, University of Sassari, Sassari, 07100, Italy

    Massimo Tistarelli

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

    XueJun Li

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  1. YenLung Lai
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  2. XingBo Dong
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Correspondence to Zhe Jin or Wei Jia.

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Lai, Y., Dong, X., Jin, Z. et al. Rethinking Contemporary Deep Learning Techniques for Error Correction in Biometric Data. Int J Comput Vis 133, 2158–2175 (2025). https://doi.org/10.1007/s11263-024-02280-8

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