Deep Learning Based Fuzzy Extractor for Generating Strong Keys from Biometric Face Images

Biometric techniques have traditionally been used in various cybersecurity applications. For instance, some user authentication systems use biometric facial images, fingerprints, iris images, vein patterns, and much more. Most of these applications store biometric features copies (or data derived from these features). Authentication is carried out based on the results of comparing the presented biometric images with the reference ones. However, if the storage is compromised, biometric personal data will be lost and this significantly limits the scope of biometric techniques. Fuzzy extractors solve this problem. Instead of reference biometric data, fuzzy extractors extract cryptographically strong keys (secret bit strings, passwords) that are used to authenticate users. In addition, the extracted keys can be used as a source of entropy for various cryptographic mechanisms (encryption, electronic signature, etc.). In this paper, we propose a fuzzy extractor for generating cryptographically strong keys from biometric images of a human face. Our extractor uses biometric image preprocessing using deep learning methods, as well as code-based cryptosystems that provide a post-quantum (quantum-resistant) level of security.