Motion Tolerant Finger Vein Authentication using Deep Learning Techniques
Keywords:
Dataset Augmentation, Finger Vein Authentication, Finger Vein Image Labelling, LSTM, VGG16Abstract
Finger vein authentication offers enhanced security due to the unique and internal nature of vein patterns. However, real-world applications encounter significant issues from motion artifacts and varying image capture conditions, impacting performance and reliability. This study addresses these challenges by utilizing image labelling, dataset augmentation, and a motion-tolerant deep learning architecture. Pixel-wise labelling of finger vein images enhances the model's sensitivity to vein patterns, facilitating data augmentation at the pixel level and improving robustness to environmental variations. The data is enhanced using extensive data augmentation techniques. The proposed methodology combines “Convolutional Neural Networks (CNN)” and “Long Short-Term Memory (LSTM)” for feature extraction and handling motion artifacts. CNN effectively captures spatial features while the LSTM processes temporal information, making the model more resilient to motion artifacts. The model is designed to adapt to different lighting conditions and handle variations in finger positioning, ensuring accurate recognition. This comprehensive approach significantly improves the reliability and performance of finger vein authentication systems in diverse real-world environments.
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Liu, C. Y., Ruan, S. J., Lai, Y. R., & Yao, C. Y. (2019). Finger-vein as a biometric-based authentication. IEEE Consumer Electronics Magazine, 8(6), 29-34.
Shaheed, K., Mao, A., Qureshi, I., Kumar, M., Hussain, S., & Zhang, X. (2022). Recent advancements in finger vein recognition technology: methodology, challenges and opportunities. Information Fusion, 79, 84-109.
Wang, K., Ma, H., Popoola, O. P., & Li, J. (2011). Finger vein recognition. Biometrics, 31-53.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 5 Sep 2014.
Hochreiter, S., & Schmidhuber, J. Long short-term memory. Neural computation, 9(8), 1735-1780, 1997.
Zhang, K., Chao, W. L., Sha, F., & Grauman, K. Video summarization with long short-term memory. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14 (pp. 766-782). Springer International Publishing.
Qin, H., & Wang, P. Finger-vein verification based on LSTM recurrent neural networks. Applied Sciences, 9(8), 1687, 2019
Kauba, C., & Uhl, A. An available open-source vein recognition framework. Handbook of Vascular Biometrics, 113-142, 2020
A. Mathew, P. Amudha, and S. Sivakumari, "Finger Vein Image Labelling using Hybrid Algorithm," International Conference on Emerging Trends in Industry 4.0 and Sustainable Concepts, 2023, pp. 268-271
Scantlebury, J., Brown, N., Von Delft, F., & Deane, C. M. Data set augmentation allows deep learning-based virtual screening to better generalize to unseen target classes and highlight important binding interactions. Journal of chemical information and modeling, 60(8), 3722-3730,2020
Shorten, C., & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48, 2019
Mikołajczyk, A., & Grochowski, M. Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW) (pp. 117-122). IEEE, May 2018
Taylor, L., & Nitschke, G. Improving deep learning with generic data augmentation. In 2018 IEEE symposium series on computational intelligence (SSCI) (pp. 1542-1547). IEEE, November 2018.
Perez, L., & Wang, J. The effectiveness of data augmentation in image classification using deep learning, 2017. arXiv preprint arXiv:1712.04621.
Kuzu, R. S., Piciucco, E., Maiorana, E., & Campisi, P. On-the-fly finger-vein-based biometric recognition using deep neural networks. IEEE Transactions on information Forensics and Security, 15, 2641-2654, 2020.
Y. Yamagishi and S. Maeda, "Deep Learning of Convolutional Neural Networks for Finger Vein Authentication," IET Biometrics, vol. 6, no. 3, pp. 217-226, 2017.
S. A. Radzi, M. Khalil-Hani, and R. Bakhteri, "Finger-vein biometric identification using convolutional neural network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 24, no. 3, pp. 1863-1878, 2016.
H. Qin and M. A. El-Yacoubi, "Deep representation-based feature extraction and recovering for finger-vein verification," IEEE Transactions on Information Forensics and Security, vol. 12, no. 8, pp. 1816-1829, Aug. 2017.
H. G. Hong, M. B. Lee, and K. R. Park, "Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors," Sensors, vol. 17, no. 6, pp. 1297, 2017.
E. Jalilian and A. Uhl, "Finger-vein recognition using deep fully convolutional neural semantic segmentation networks: The impact of training data," in IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China, 2018, pp. 1-7.
Y. Fang, Q. Wu, and W. Kang, "A novel finger vein verification system based on two-stream convolutional network learning," Neurocomputing, vol. 290, pp. 132-143, May 2018.
W. Kim, J. M. Song, and K. R. Park, "Multimodal biometric recognition based on convolutional neural network by the fusion of finger-vein and finger shape using near-infrared (NIR) camera sensor," Sensors, vol. 18, no. 1, pp. 101-114, Jan. 2018.
M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, "GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification," Neurocomputing, vol. 321, pp. 321-331, 2018.
C. Bowles, L. Chen, R. Guerrero, P. Bentley, B. Glocker, and D. Rueckert, "GAN augmentation: Augmenting training data using generative adversarial networks," arXiv preprint arXiv:1810.10863, 2018.
R. S. Kuzu, E. Piciucco, E. Maiorana, and P. Campisi, "On-the-fly finger-vein-based biometric recognition using deep neural networks," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3104-3118, 2020.
Yin, Y., Liu, L., Sun, X. (2011). SDUMLA-HMT: A Multimodal Biometric Database. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg.
Tsinghua University Finger Vein and Finger Dorsal Texture Database（THU-FVFDT）. (2014). [Dataset]. In Wenming Yang.
Mirza M, Osindero S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. Nov 6, 2014.
Xie C, Kumar A. Finger vein identification using convolutional neural network and supervised discrete hashing. Deep Learning for Biometrics. 2017, pp.109-32.
Bilal, A., Sun, G. and Mazhar, S., Finger-vein recognition using a novel enhancement method with convolutional neural network. Journal of the Chinese Institute of Engineers, 44(5), 2021, pp.407-417.
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