Cryptographic Key Generation Using Deep Learning with Biometric Face and Finger Vein Data

Tsehayu Yirga ^1,2,3,4* HAILU YIRGA ⁵ Eshetie Addisu ^6,7

• ¹ Mekdela Amba University, Dessie, Ethiopia
• ² College of Natural and Computational Science, Mekdela Amba University, Tulu Awuliya, Ethiopia
• ³ Department of computer science, college of natural and computational science, Mekdela Amba University, Mekdela Amba, Amhara, Dessie, Ethiopia
• ⁴ Department of computer science, college of natural and computational science, Mekdela Amba University, Mekdela Amba, Amhara, Ethiopia, Dessie, Ethiopia
• ⁵ University of Gondar, Gondar, Amhara, Ethiopia
• ⁶ College of Informatics, University of Gondar, Gondar, Amhara Region, Ethiopia
• ⁷ Department of information systems, College of informatics, University of Gondar, Gondar, Amhara, Ethiopia, Gondar, Ethiopia

This research proposes a novel approach to cryptographic key generation using biometric data from face and finger vein modalities, enhanced by deep learning techniques. By using pretrained models FaceNet and VGG19 for feature extraction and employing a Siamese Neural Network (SNN), the study demonstrates the integration of multimodal biometrics with fuzzy extractors to create secure and reproducible cryptographic keys. Feature fusion techniques, combined with preprocessing and thresholding, ensure robust feature extraction and conversion to binary formats for key generation. The model demonstrates impressive accuracy with a vector converter, achieving a sigma similarity of 93% and sigma difference of 64.0%. Evaluation metrics, including False Acceptance Rate (FAR) and False Rejection Rate (FRR), indicate significant improvements, achieving FRR < 3.4% and FAR < 1%, outperforming previous works. Additionally, the adoption of Goppa code-based cryptographic systems ensures post-quantum security. This work not only enhances biometric cryptography's accuracy and resilience but also paves the way for future exploration of quantum-resistant and scalable systems.