Volume 21 • Issue 2 • PP: 119-148 • 2026
GLU-Attention Hybrid Architecture for Dual-Biometric Passkey Generation via Neuro-Symbolic and Chaotic Dynamics
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The generation of cryptographic keys from biometric traits presents an opportunity to replace traditional password-based systems with mechanisms grounded in individual physiology. Nonetheless, reliably deriving secure and reproducible keys from modalities such as fingerprints and irises remains a significant challenge, particularly under varying input conditions and constraints on entropy. In this work, we present a hybrid dual-path deep learning architecture that combines Gated Linear Units (GLUs) with Squeeze-and-Excitation (SE) modules to extract rich, multimodal embeddings from iris and fingerprint images. The model, trained on an augmented cross-modal dataset, achieved a test accuracy of 99.92% and consistently high F1-scores across 50 subjects. To derive the cryptographic key, we apply a multi-stage pipeline that blends principal component projections, distance-based feature encoding, chaotic sequence modeling based on Lorenz-like dynamics, and a lightweight error-correcting routine. These representations are fused via a custom mixing function, producing a 512-bit binary vector subsequently refined using a SHA-256-based HKDF. Evaluation of the generated keys indicates near-ideal entropy, high inter-user separation, and strong avalanche characteristics. The system also passed multiple NIST statistical randomness tests and achieved a near-zero false acceptance rate. These results support the feasibility of the proposed method for secure and repeatable biometric key generation.
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References
[1] F. Corella, “Overcoming the UX challenges faced by FIDO credentials in the consumer space,” in Proc. Lect. Notes Comput. Sci., 2023, doi: 10.1007/978-3-031-35822-7_30.
[2] J. M. - and G. B. K. -, “Detection of fake biometrics - assessment of image quality in face, fingerprint,” Int. J. Multidiscipl. Res., vol. 6, no. 1, 2024, doi: 10.36948/ijfmr.2024.v06i01.12063.
[3] K. Yasunaga and K. Yuzawa, “On the limitations of computational fuzzy extractors,” IEICE Trans. Fundamentals Electron., Commun. Comput. Sci., vol. E106A, no. 3, 2023, doi: 10.1587/transfun.2022CIL0001.
[4] Mehmood, A. Shafique, M. Alawida, and A. N. Khan, “Advances and vulnerabilities in modern cryptographic techniques: A comprehensive survey on cybersecurity in the domain of machine/deep learning and quantum techniques,” IEEE Access, vol. 12, 2024, doi: 10.1109/ACCESS.2024.3367232.
[5] S. A. El-Rahman and A. S. Alluhaidan, “Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments,” PLoS One, vol. 19, no. 2, 2024, doi: 10.1371/journal.pone.0291084.
[6] P. Dash, F. Pandey, M. Sarma, and D. Samanta, “Efficient private key generation from iris data for privacy and security applications,” J. Inf. Secur. Appl., vol. 75, 2023, doi: 10.1016/j.jisa.2023.103506.
[7] A. AbdulRaheeM and S. A. Hasso, “Generate and evaluate encryption keys obtained from iris biometric data,” in *Proc. 21st Int. Multi-Conf. Syst., Signals Devices (SSD)*, Apr. 2024, pp. 321–328, doi: 10.1109/SSD61670.2024.10548985.
[8] L. Chao, T. Nazaré, and E. Nepomuceno, “Key generation from fingerprint biometric,” in Proc. 15th IEEE Int. Conf. Ind. Appl. (INDUSCON), Nov. 2023, pp. 611–612, doi: 10.1109/INDUSCON58041.2023.10374712.
[9] Z. I. A. Al-Rifaee, T. Z. Ismaeel, and S. I. Abood, “Cryptography based on fingerprint bio metrics,” J. Internet Serv. Inf. Secur., vol. 14, no. 4, pp. 401–417, Nov. 2024, doi: 10.58346/JISIS.2024.I4.025.
[10] P. Dash, M. Sarma, and D. Samanta, “Fractal-based approach to secure key generation from fingerprint and iris biometrics,” in Proc. Lect. Notes Comput. Sci., 2024, pp. 99–111, doi: 10.1007/978-3-031-58181-6_9.
[11] D. K. Vallabhadas and M. Sandhya, “Cancelable bimodal shell using fingerprint and iris,” J. Electron. Imaging, vol. 32, no. 6, Dec. 2023, doi: 10.1117/1.JEI.32.6.063027.
[12] B. Wang et al., “High-security dual-image encryption based on fingerprint key with strong robustness,” Optik, vol. 288, p. 171245, Oct. 2023, doi: 10.1016/j.ijleo.2023.171245.
[13] R. Sridevi and P. Shobana, “Multimodal security of iris and fingerprint with bloom filters,” arXiv, Jun. 2024.
[14] K. N. Singh, N. Baranwal, O. P. Singh, and A. K. Singh, “DeepENC: Deep learning-based ROI selection for encryption of medical images through key generation with multimodal information fusion,” IEEE Trans. Consum. Electron., vol. 70, no. 3, pp. 6149–6156, Aug. 2024, doi: 10.1109/TCE.2024.3406963.
[15] B. Wang et al., “A multiple-image encryption method based on bimodal biometric keys,” Opt. Commun., vol. 565, p. 130651, Aug. 2024, doi: 10.1016/j.optcom.2024.130651.
[16] J. Muhammad, Y. Wang, J. Hu, K. Zhang, and Z. Sun, “CASIA-Iris-Africa: A large-scale African iris image database,” Mach. Intell. Res., vol. 21, no. 2, 2024, doi: 10.1007/s11633-022-1402-8.
[17] N. K. Sreeja, “A hierarchical heterogeneous ant colony optimization based fingerprint recognition system,” Intell. Syst. Appl., vol. 17, 2023, doi: 10.1016/j.iswa.2023.200180.
[18] J. Ren, C. Li, Y. An, W. Zhang, and C. Sun, “Few-shot fine-grained image classification: A comprehensive review,” AI, vol. 5, no. 1, 2024, doi: 10.3390/ai5010020.
[19] C. Liu, J. Zhen, and W. Shan, “Time series classification based on convolutional network with a gated linear units kernel,” Eng. Appl. Artif. Intell., vol. 123, 2023, doi: 10.1016/j.engappai.2023.106296.
[20] T. Kumar, S. Bhushan, and S. Jangra, “Ann trained and WOA optimized feature-level fusion of iris and fingerprint,” Mater. Today Proc., vol. 51, pp. 1–11, 2022, doi: 10.1016/j.matpr.2021.03.604.
[21] C. Kamlaskar and A. Abhyankar, “Iris-fingerprint multimodal biometric system based on optimal feature level fusion model,” AIMS Electron. Electr. Eng., vol. 5, no. 4, pp. 229–250, 2021, doi: 10.3934/electreng.2021013.
[22] Jagadeesan and K. Duraiswamy, “Secured cryptographic key generation from multimodal biometrics: Feature level fusion of fingerprint and iris,” Int. J. Comput. Sci. Inf. Secur., vol. 7, no. 1, 2010.
[23] Almomani et al., “Proposed biometric security system based on deep learning and chaos algorithms,” Comput., Mater. Contin., vol. 74, no. 2, pp. 3515–3537, 2023, doi: 10.32604/cmc.2023.033765.
[24] T. G. Yirga, H. G. Yirga, and E. G. Addisu, “Cryptographic key generation using deep learning with biometric face and finger vein data,” Front. Artif. Intell., vol. 8, Apr. 2025, doi: 10.3389/frai.2025.1545946.
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