Mask-Resilient Multi-Biometric Authentication for Healthcare
Article Sidebar
Main Article Content
Abstract
The fast integration of digital healthcare platforms, including telemedicine and electronic health records, has created a significant need for secure, contactless authentication methods. Traditional login methods such as passwords and PINs are increasingly insufficient for ensuring the security of sensitive patient data. This paper presents a robust, lightweight Healthcare Authentication System that five biometric modules to address major challenges in facial recognition: spoofing attacks, liveness detection, and facial occlusion due to mask-wearing. The system leverages RetinaFace for face detection, a MobileNetV2 model for mask classification, a lightweight CNN for blink-based liveness verification, and MiniFASNet for spoof detection. For identity matching, the system utilizes InceptionResNetV1 with ArcFace loss, enabling effective recognition of both masked and unmasked faces through a dual-embedding strategy. Each module was evaluated using benchmark datasets: CASIA-FASD for spoofing, RMFD for mask detection, open/closed eye datasets for blink detection, and VGGFace2 (with synthetic masking) for recognition. The system achieved 91% accuracy in blink detection, 85% in spoof detection, 97% in mask classification, and 96% in face recognition. The complete pipeline achieved 88% overall accuracy on the evaluated dataset. The proposed solution offers a practical and scalable approach for secure biometric authentication in healthcare environments, particularly in clinical settings where hygiene, user accessibility, and anti-spoof measures are essential. This work provides a foundation for future research in offline biometric verification, depth-based spoof detection, and multilingual interface design, contributing toward more inclusive and secure healthcare technologies. While we do not fine-tune model backbones, our contribution lies in a clinically viable integration strategy that combines blink-based liveness, anti-spoofing, mask detection, and face verification under a fixed operating point with identity-disjoint evaluation, achieving 88% end-to-end accuracy on public-dataset inputs.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
[1] Z. A. Bahman, “Biometrics in healthcare as a technological contribution to mitigate patient identification errors: A review,” International Journal of Development Research, pp. 64930–64937, 2024, doi: 10.37118/ijdr.27834.02.2024.
[2] Pew Research Center, “Pew Research Center,” 2024. [Online]. Available: https://www.pewresearch.org/
[3] Y. Atoum, Y. Liu, A. Jourabloo, and X. Liu, “Face anti-spoofing using patch and depth-based CNNs,” 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 319–328, 2017, doi: 10.1109/btas.2017.8272713.
[4] D. N. Nishanth and G. Mallikarjuna Rao, “Liveness detection based on human eye blinking for photo attacks,” International Journal of Engineering and Advanced Technology, vol. 9, no. 1, pp. 4074–4077, 2019, doi: 10.35940/ijeat.a1314.109119.
[5] M. Ngan, P. Grother, and K. Hanaoka, “Ongoing face recognition vendor test (FRVT) part 6A,” NIST Interagency Report, 2020, doi: 10.6028/NIST.IR.8311.
[6] D. Montero, M. Nieto, P. Leskovsky, and N. Aginako, “Boosting masked face recognition with multi-task ArcFace,” arXiv preprint, 2022, doi: 10.1109/SITIS57111.2022.00042.
[7] S. Shuhui and M. Xiaochen, “Dual-proxy modeling for masked face recognition,” Procedia Computer Science, vol. 208, pp. 145–151, 2022, doi: 10.1016/j.procs.2022.10.022.
[8] Y. Himeur et al., “Face mask detection in smart cities using deep and transfer learning: Lessons learned from the COVID-19 pandemic,” Systems, vol. 11, no. 2, pp. 107, 2023, doi: 10.3390/systems11020107.
[9] M. I. Razzak, M. Imran, and G. Xu, “Big data analytics for preventive medicine,” Neural Computing and Applications, vol. 32, no. 9, pp. 4417–4451, 2019, doi: 10.1007/s00521-019-04095-y.
[10] A. Holzinger, C. Biemann, C. S. Pattichis, and D. B. Kell, “What do we need to build explainable AI systems for the medical domain?” arXiv preprint arXiv:1712.09923, 2017. [Online]. Available: https://arxiv.org/abs/1712.09923
[11] Z. Qin et al., “A survey of identity recognition via data fusion and feature learning,” Information Fusion, 2022, doi: 10.1016/j.inffus.2022.10.032.
[12] Z. Yu et al., “Searching central difference convolutional networks for face anti-spoofing,” arXiv preprint, 2020, doi: 10.1109/CVPR42600.2020.00534.
[13] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 4510–4520, 2018, doi: 10.1109/CVPR.2018.00474.
[14] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the impact of residual connections on learning,” in AAAI'17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, vol. 31, no. 1, pp. 4278–4284, 2017.
[15] J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 4685–4694, 2019, doi: 10.1109/CVPR.2019.00482.
[16] Z. Wang, B. Huang, G. Wang, P. Yi, and K. Jiang, “Masked face recognition dataset and application,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 5, no. 2, pp. 298–304, April 2023, doi: 10.1109/TBIOM.2023.3242085.
[17] A. Cabani, K. Hammoudi, H. Benhabiles, and M. Melkemi, “MaskedFace-Net – A dataset of correctly/incorrectly masked face images in the context of COVID-19,” Smart Health, vol. 19, pp. 100144, 2021, doi: 10.1016/j.smhl.2020.100144.
[18] N. D. Lane et al., “DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices,” 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Vienna, Austria, pp. 1–12, 2016, doi: 10.1109/IPSN.2016.7460664.
[19] J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou, “RetinaFace: Single-shot multi-level face localisation in the wild,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 5202–5211, 2020, doi: 10.1109/CVPR42600.2020.00525.
[20] X. Zhang, X. Zhou, M. Lin, and J. Sun, “ShuffleNet: An extremely efficient convolutional neural network for mobile devices,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 6848–6856, 2018, doi: 10.1109/CVPR.2018.00716.
[21] P. Chitrapu, M. K. Morampudi, and H. K. Kalluri, “A secure and explainable multimodal biometric system using trust adaptive fusion for face and fingerprint,” Scientific Reports, vol. 16, p. 14244, 2026, doi: 10.1038/s41598-026-43252-x.
[22] A. Al Abdulwahid, “Privacy-aware continuous federated biometric authentication (PACFBA) using federated deep learning: A novel usable security framework,” Scientific Reports, 2026, doi: 10.1038/s41598-026-55046-2.