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. Bio-cryptosystems 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 (BC) named WiFaKey, for generating cryptographic keys from face in unconstrained settings. Specifically, WiFaKey first introduces an adaptive random masking-driven feature transformation pipeline, AdaMTrans. AdaMTrans effectively quantizes and binarizes real-valued features and incorporates an adaptive random masking scheme to align the bit error rate (BER) 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 nonlearning 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 labeled faces in the wild database (LFW) dataset, WiFaKey achieved an average genuine match rate (GMR) of 85.45% and 85.20% at a 0% false match rate (FMR) for MagFace and AdaFace features, respectively. Our comprehensive comparative analysis shows a significant performance improvement of WiFaKey.