References
- N. Hu, H. Ma, and T. Zhan, “Finger vein biometric verification using block multi-scale uniform local binary pattern features and block twodirectional two-dimension principal component analysis,” Optik, vol. 208, Apr. 2020, Art. no. 163664. https://doi.org/10.1016/j.ijleo.2019.163664
- D. Fronitasari and D. Gunawan, “Palm vein recognition by using modified of local binary pattern (LBP) for extraction feature,” in Proceedings of the 15th International Conference on Quality in Research (QiR): International symposium on electrical and computer engineering, Nusa Dua, Bali, Indonesia, Jul. 2017, pp. 18–22. https://doi.org/10.1109/QIR.2017.8168444
- V. Ponnusamy, A. Sridhar, A. Baalaaji, and M. Sangeetha, “A palm vein recognition system based on a support vector machine,” IEIE Transactions on Smart Processing & Computing, vol. 8, no. 1, Feb. 2019, pp. 1–7. http://doi.org/10.5573/IEIESPC.2019.8.1.001
- Y. D. Wang, Q. Y. Yan, and K. F. Li, “Hand vein recognition based on multi-scale LBP and wavelet,” in Proceedings of the 2011 International Conference on Wavelet Analysis and Pattern Recognition, Guilin, China, Jul. 2011, pp. 214–218. https://doi.org/10.1109/ICWAPR.2011.6014480
- X. Zhang, and W. Wang, “Finger vein recognition method based on GLCM-HOG and SVM,” in Proceedings of the 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China, Sep. 2020, pp. 698–701. https://doi.org/10.1109/ICISCAE51034.2020.9236798
- H. Kuang, Z. Zhong, X. Liu, and X. Ma, “Palm vein recognition using convolution neural network based on feature fusion with HOG feature,” in Proceedings of the 5th International Conference on Smart Grid and Electrical Automation (ICSGEA), Zhangjiajie, China, Jun. 2020, pp. 295–299. https://doi.org/10.1109/ICSGEA51094.2020.00070
- X. Ma, X. Jing, H. Huang, Y. Cui, and J. Mu, “Palm vein recognition scheme based on an adaptive Gabor filter,” Iet Biometrics, vol. 6, no. 5, Jan. 2017, pp. 325–333. https://doi.org/10.1049/iet-bmt.2016.0085
- J. Yang, Y. Shi, and J. Yang, “Finger-vein recognition based on a bank of Gabor filters,” in Computer Vision–ACCV 2009: 9th Asian Conference on Computer Vision, Xi’an, Sep. 2009, Revised Selected Papers, Part I 9, 2010, pp. 374–383. https://doi.org/10.1007/978-3-642-12307-8_35
- A. Krizhevsky, I. Sutskever, and G.E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 25, 2012, pp. 1097–105. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
- K. Simonyan, and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, Sep. 2014. https://doi.org/10.48550/arXiv.1409.1556
- I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, “Generative adversarial nets,” Advances in Neural Information Processing Systems, vol. 27, 2014, pp. 2672–2680. https://proceedings.neurips.cc/paper_files/paper/2014/file/f033ed80deb0234979a61f95710dbe25-Paper.pdf
- H. Yang, P. Fang, and Z. Hao, “A GAN-based method for generating finger vein dataset,” in Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence, Dec. 2020, pp. 1–6. https://doi.org/10.1145/3446132.3446150
- J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” Advances in Neural Information Processing Systems, vol. 27, 2014. https://proceedings.neurips.cc/paper_files/paper/2014/file/532a2f85b6977104bc93f8580abbb330-Paper.pdf
- A.N. Kolmogorov, “On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables,” in American Mathematical Society Translations – Series 2, vol. 17, 1961, pp. 369–373.
- A.N. Kolmogorov, “On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition,” American Mathematical Society Translations, vol. 2, no. 28, 1963, pp. 55–59.
- Z. Liu, Y. Wang, S. Vaidya, F. Ruehle, J. Halverson, M. Soljačić, T.Y. Hou, and M. Tegmark, “KAN: Kolmogorov–Arnold networks,” arXiv preprint arXiv:2404.19756, Apr. 2024. https://doi.org/10.48550/arXiv.2404.19756
- J. Wang, P. Cai, Z. Wang, H. Zhang, and J. Huang, “CEST-KAN: Kolmogorov–Arnold networks for CEST MRI data analysis,” arXiv preprint arXiv:2406.16026, Jun. 2024. https://doi.org/10.48550/arXiv.2406.16026
- Z. Huang, J. Cui, L. Yu, L.F. Herbozo Contreras, and O. Kavehei, “Abnormality detection in time-series bio-signals using Kolmogorov– Arnold networks for resource-constrained devices,” medRxiv, 2024-06, 2024. https://doi.org/10.1101/2024.06.04.24308428
- C. Li, X. Liu, W. Li, C. Wang, H. Liu, Y. Liu, Z. Chen, and Y. Yuan, “UKAN makes strong backbone for medical image segmentation and generation,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 5, Apr. 2025, pp. 4652–4660. https://doi.org/10.1609/aaai.v39i5.32491
- M.S.M. Asaari, S.A. Suandi, and B.A. Rosdi, “Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics,” Expert Systems with Applications, vol. 41, no. 7, pp. 3367–3382, Jun. 2014. https://doi.org/10.1016/j.eswa.2013.11.033
- Y. Yin, L. Lili, and S. Xiwei, “SDUMLA-HMT: A multimodal biometric database,” in Biometric Recognition: 6th Chinese Conference, CCBR 2011, Beijing, China, Dec. 2011. Proceedings 6. Springer Berlin Heidelberg, 2011, pp. 260–268. https://doi.org/10.1007/978-3-642-25449-9_33