Enhancing Finger Vein Authentication Security: A Texture Descriptor-Based Approach to Counter Spoofing Attacks
Keywords:
Finger Vein Authentication, Biometric Security, Spoofing Attacks, Texture Descriptors, Local Binary Patterns, Presentation Attack Detection.Abstract
Spoofing attacks in biometric systems pose significant challenges to security and reliability, especially in the context of finger vein recognition. This paper investigates spoofing techniques targeting finger vein authentication systems and explores the effectiveness of texture descriptors in counteracting such attacks. We propose a novel approach that utilizes advanced texture descriptors, such as Local Binary Patterns (LBP), Gabor filters, and Gray-Level Co-occurrence Matrix (GLCM), to capture the unique textural features of authentic finger vein patterns. These descriptors are employed to differentiate between genuine and spoofed finger vein images, enhancing the robustness of the system against presentation attacks. Our experimental results demonstrate that texture descriptors can significantly improve the accuracy of finger vein recognition systems, effectively identifying counterfeit or altered finger vein patterns, and mitigating spoofing risks. The proposed method offers a promising solution to enhance the security of biometric authentication systems, providing a higher level of protection against fraudulent attempts.
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Jain, A. K., Nandakumar, K., & Ross, A. (2008). Fingerprint matching using minutiae and texture features. IEEE Transactions on Image Processing, 17(7), 1167-1178. https://doi.org/10.1109/TIP.2008.2000791
Zhang, L., Tan, T., & Li, S. (2015). A survey on fingerprint recognition systems. Journal of Computer Science and Technology, 20(1), 56-69.
Wang, J., Zhang, Y., & Li, H. (2019). Robust finger vein spoof detection using local binary patterns. Pattern Recognition Letters, 123, 72-78. https://doi.org/10.1016/j.patrec.2019.03.016
Xie, X., & Wu, Q. (2015). Finger vein recognition using Gabor wavelet features and support vector machines. Proceedings of the International Conference on Image Processing (ICIP), 2241-2245. https://doi.org/10.1109/ICIP.2015.7351637
Shubham Malhotra, Muhammad Saqib, Dipkumar Mehta, and Hassan Tariq. (2023). Efficient Algorithms for Parallel Dynamic Graph Processing: A Study of Techniques and Applications. International Journal of Communication Networks and Information Security (IJCNIS), 15(2), 519–534. Retrieved from https://ijcnis.org/index.php/ijcnis/article/view/7990
Chen, J., & Li, X. (2020). A novel approach to spoof detection in biometric systems. Journal of Electronics & Information Technology, 42(3), 592-599. https://doi.org/10.11999/JEIT190579
Zhang, L., Tan, T., & Li, S. (2015). A survey on fingerprint recognition systems. Journal of Computer Science and Technology, 20(1), 56-69. https://doi.org/10.1007/s11390-015-1509-1
Kang, J., & Cho, Y. (2021). Finger vein spoof detection using deep learning with texture analysis. IEEE Transactions on Information Forensics and Security, 16, 114-125. https://doi.org/10.1109/TIFS.2020.3007744
Li, H., & Du, L. (2016). A new method for finger vein recognition and spoof detection using deep neural networks. IEEE Access, 4, 13198-13208. https://doi.org/10.1109/ACCESS.2016.2606037
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