WiFaKey: Generating Cryptographic Keys from Face in the Wild
<|reference_start|>WiFaKey: Generating Cryptographic Keys from Face in the Wild: Deriving a unique cryptographic key from biometric measurements is a challenging task due to the existing noise gap between the biometric measurements and error correction coding. Additionally, privacy and security concerns arise as biometric measurements are inherently linked to the user. Biocryptosystems represent a key branch of solutions aimed at addressing these issues. However, many existing bio-cryptosystems rely on handcrafted feature extractors and error correction codes (ECC), often leading to performance degradation. To address these challenges and improve the reliability of biometric measurements, we propose a novel biometric cryptosystem named WiFaKey, for generating cryptographic keys from face in unconstrained settings. Speciffcally, WiFaKey ffrst introduces an adaptive random masking-driven feature transformation pipeline, AdaMTrans. AdaMTrans effectively quantizes and binarizes realvalued features and incorporates an adaptive random masking scheme to align the bit error rate with error correction requirements, thereby mitigating the noise gap. Besides, WiFaKey incorporates a supervised learning-based neural decoding scheme called Neural-MS decoder, which delivers a more robust error correction performance with less iteration than non-learning decoders, thereby alleviating the performance degradation. We evaluated WiFaKey using widely adopted face feature extractors on six large unconstrained and two constrained datasets. On the LFW dataset, WiFaKey achieved an average Genuine Match Rate of 85.45% and 85.20% at a 0% False Match Rate for MagFace and AdaFace features, respectively. Our comprehensive comparative analysis shows a signiffcant performance improvement of WiFaKey. The source code of our work is available at github.com/xingbod/WiFaKey.<|reference_end|>
@article{dong2024wifakey:,
    title={WiFaKey: Generating Cryptographic Keys from Face in the Wild},
    author={Xingbo Dong, Hui Zhang, Yen Lung Lai, Zhe Jin, Junduan Huang, Wenxiong
  Kang, Andrew Beng Jin Teoh},
    journal={arXiv preprint arXiv:2407.14804},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14804},
    primaryClass={cs.CR}
}