the Finger Vein Recognition Using Deep Learning Technique
DOI:
https://doi.org/10.31185/wjcms.43Keywords:
Finger Vein RecognitionAbstract
Finger vein biometrics have gained a lot of attention in recent years because they offer the perfect balance of security and economic viability, with advantages such as being the least susceptible to identity theft because veins are present beneath the skin, being unaffected by ageing of the person, etc. All of these factors make it necessary to create functioning models to meet the ever-increasing need for security. The use of facial recognition and AI-based biometrics, particularly in law enforcement, has become a hot topic because of its inadvertent demographic bias. Biometric bias, on the other hand, has far-reaching consequences that extend into daily use cases. When an ATM transaction or an online banking transaction is compromised by a false positive or negative verification, fraudulent activity is made easier. The study in this research work focused on the difficulty of determining the veins of a fingertip. On two widely used and freely available datasets of finger veins, we applied deep convolutional neural network models to feature extraction. Finger vein recognition has gotten a lot of interest recently as a novel biometric technique. Finger vein recognition might benefit from applying deep learning, an end-to-end approach that has shown promising results in sectors like face recognition and target detection.
References
Y. Fang, Q. Wu, and W. Kang, “A novel finger vein verification system based on two-stream convolutional network learning,” Neurocomputing, vol. 290, pp. 100–107, 2018.
M. & Ferguson, R. & Tina Ak, Lee, & H Yung-Tsun, and K. Law, “Automatic localization of casting defects with convolutional neural networks,” 2017.
H. G. Hong, M. B. Lee, and K. R. Park, “Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors. Sensors (Basel),” 2017.
W. Liu, W. Li, L. Sun, L. Zhang, and P. Chen, “Finger vein recognition based on deep learning,” 12th IEEE conference on industrial electronics and applications (ICIEA), pp. 205–210, 2017.
O. A. Hassen, “Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis,” Journal of Southwest Jiaotong University, vol. 54, 2019.
Y. Liu, J. Ling, Z. Liu, J. Shen, and C. Gao, “Finger vein secure biometric template generation based on deep learning,” Soft Computing, vol. 22, no. 7, pp. 2257–2265, 2018.
G. Meng, P. Fang, and B. Zhang, “Finger vein recognition based on convolutional neural network,” MATEC Web of Conferences EDP Sciences, 2017.
T. D. Pham, Y. H. Park, D. T. Nguyen, S. Y. Kwon, and K. R. Park, “Nonintrusive Finger-Vein Recognition System Using NIR Image Sensor and Accuracy Analyses According to Various Factors. Sensors (Basel),” 2015.
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, 2017.
A. Hassen, A. A. Abdulhussein, M. Saad, Z. A. Darwish, S. Othman, Y. A. Tiun, and Lotfy Towards a Secure Signature Scheme Based on Multimodal Biometric Technology: Application for IOT Blockchain Network, vol. 12, pp. 1699–1699, 2020.
S.-H. Database. http://mla.sdu.edu.cn/info/1006/1195.htm.
K. & Shaheed, Id, & Hangang, & Liu, Yang, & Gongping, Qureshi, & Imran, and Gou, “Jie & Yin, Yilong. information A Systematic Review of Finger Vein Recognition Techniques. Information (Switzerland),” 2018.
A. Ansam and H. K. Ibrahim, “A Pragmatic Review and Analytics of Gait Recognition Techniques in Biometric Domain of Research,” International Journal of Computing and Business Research (IJCBR), vol. 10, no. 3, pp. 1–9, 2020.
G. K. Sidiropoulos, P. Kiratsa, P. Chatzipetrou, and G. A. Papakostas, “Feature Extraction for Finger-Vein-Based Identity Recognition,” Journal of Imaging, vol. 7, no. 5, pp. 89–89, 2021.
K. & Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” 2014.
A. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. Circ. Syst. Video Tech, vol. 14, pp. 4–20, 2004.
A. K. Jain, J. Feng, and K. Nandakumar Computer, vol. 43, pp. 36–44, 2010.
Z. Guo, D. Zhang, L. Zhang, and W. Zuo, “Palmprint verification using binary orientation co-occurrence vector,” Patt. Recogn. Lett, vol. 30, pp. 1219–1227, 2009.
K. Ito, H. Nakajima, K. Kobayashi, T. Aoki, and T. Higuchi, “A fingerprint matching algorithm using phase-only correlation,” IEICE Trans. Fundament. Electron. Commun. Comput. Sci, pp. 87–682, 2004.
A. Abu, N. A. Abidin, Z. Z. Darwish, and S. M, “A New Descriptor for Smile Classification Based on Cascade Classifier in Unconstrained Scenarios,” Symmetry, vol. 13, no. 5, pp. 805–805, 2021.
Y. Yin, L. Liu, and X. Sun, “SDUMLA-HMT: A Multimodal Biometric Database,” in The 6th Chinese Conference on Biometric Recognition (CCBR 2011), vol. 7098, pp. 260–268, 2011
L. Zhang, L. Zhang, D. Zhang, and H. Zhu Ensemble of local and global information for finger-knuckle-print recognition, vol. 44, 1990.
N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification,” Mach. Vision Appl, vol. 15, pp. 194–203, 2004.
T. Yanagawa, S. Aoki, and T. Ohyama, “Human finger vein images are diverse and its patterns are useful for personal identification,” MHF Preprint
Ser, vol. 12, pp. 1–7, 2007.
H. O. A. Abu, N. A. Abidin, Z. Z. Darwish, and S. M, “Realistic Smile Expression Recognition Approach Using Ensemble Classifier with Enhanced Bagging,” CMC-Computer Materials & Continue, vol. 70, no. 2, pp. 2453–2469, 2022.
Y. B. Zhang, Q. Li, J. You, and P. Bhattacharya, “Palm Vein Extraction and Matching for Personal Authentication,” in Proceedings of the 9th International Conference on Advances in Visual Information Systems, pp. 154–164, 2007.
C. B. Yu, H. F. Qin, L. Zhang, and Y. Z. Cui, “Finger-vein image recognition combining modified hausdorff distance with minutiae feature matching,” J. Biomed. Sci. Eng, vol. 2, pp. 261–272, 2009.
W. Song, T. Kim, H. C. Kim, J. H. Choi, H. J. Kong, and S. R. Lee, “A finger-vein verification system using mean curvature,” Patt. Recogn. Lett, vol. 32, pp. 1541–1547, 2011.
E. C. Lee and K. R. Park, “Image restoration of skin scattering and optical blurring for finger vein recognition,” Opt. Lasers Eng, vol. 49, pp. 816– 828, 2011.
E. C. Lee, H. Jung, and D. Kim, “New finger biometric method using near infrared imaging,” Sensors, vol. 11, pp. 2319–2333, 2011.
A. Abter, S. O. Abdulhussein, A. A. Darwish, S. M. Ibrahim, Y. M. Sheta, and W, “Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection,” CMC-COMPUTERS MATERIALS & CONTINUA, vol. 68, no. 1, pp. 961–981, 2021.
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Patt. Anal. Mach. Intell, vol. 24, pp. 971–987, 2002.
B. Zhang, Y. Gao, S. Zhao, and J. Liu, “Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor,” IEEE Trans. Image Process, vol. 19, pp. 533–544, 2010.
X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Trans. Image Process, vol. 19, pp. 1635–1650, 2010.
L. Nanni, A. Lumini, and S. Brahnam, “Local binary patterns variants as texture descriptors for medical image analysis,” Artif. Intell. Med, vol. 49, pp. 117–125, 2010.
A. Petpon and S. Srisuk, “Face Recognition with Local Line Binary Pattern,” in Proceedings of the Fifth International Conference on Image and Graphics, pp. 533–539, 2009.
G. Yang, X. Xi, and Y. Yin, “Finger vein recognition based on a personalized best bit map. Sensors (Basel),” 2012.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Sahar Wahab khadim , Hussain k. Ibrahim, Ameen Majid shadhar
This work is licensed under a Creative Commons Attribution 4.0 International License.
