Open Access
| Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
|---|---|---|
| Article Number | 01082 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/matecconf/202439201082 | |
| Published online | 18 March 2024 | |
- N.Alay, Al-Baity .HH, Deep learning approach for multimodal biometric recognition system based on fusion of iris, face, and finger vein Traits,Sensors 20, 19 (2020) [Google Scholar]
- Li. S, Zhang. B, Zhao .S, Yang. J, Local discriminant coding based convolutional feature representation for multimodal finger recognition,Information Sciences, Information Sciences, 547, 1170-1181(2021) [CrossRef] [Google Scholar]
- FO. Babalola, Y. Bitirim, Ö .Toygar. , Palm vein recognition through fusion of texturebased and CNN-based methods,Signal, Image and Video Processing, 15,459–466 (2021) [Google Scholar]
- PS .Chanukya, TK.Thivakaran, Multimodal biometric cryptosystem for human authentication using fingerprint and ear,Multimedia Tools and Applications, 79, 659–673 (2020) [Google Scholar]
- H. Shao, D. Zhong, Du .X, A deep biometric hash learning framework for three advanced hand based biometrics,IET Biometrics,10,3 (2021) [Google Scholar]
- P .Shende, Y. Dandawate, Convolutional neural network-based feature extraction using multimodal for high security application.,Evolutionary Intelligence,14,1023–1033(2021) [Google Scholar]
- RS.Kuzu, E. Maiorana, P.Campisi, Vein-based biometric verification using denselyconnected convolutional autoencoder,IEEE Signal Processing Letters, 27,1869-1873 (2020) [Google Scholar]
- Guo, G., Wechsler, H., Mobile biometrics, 3 IET, 488,2, (2017) [Google Scholar]
- Nait-Ali, A., Hidden biometrics: when biometric security meets biomedical engineering, (Springer Nature, France, 2019) [Google Scholar]
- Vatsa, M., Singh, R., Majumdar, A., Deep learning in biometrics, CRC Press, USA, 328(2018) [Google Scholar]
- Jain A.K, Ross, A.A, Nandakumar. K, Introduction to Biometrics, (Springer Science & Business Media: Berlin, Germany, 2011) [CrossRef] [Google Scholar]
- Shaheed.K, Liu. H, Yang. G, Qureshi. I, Gou. J, Yin, Y, A systematic review of finger vein recognition techniques, Informatics, 9, 213, (2018) [Google Scholar]
- Mitra, S., Gofman, M., Biometrics in a data driven world: trends, technologies, and challenges, (CRC Press, USA, 2016) [CrossRef] [Google Scholar]
- Kisku, D.R., Gupta, P., Sing, J.K. Advances in biometrics for secure human authentication and recognition, (CRC Press, USA, 2013) [CrossRef] [Google Scholar]
- H. Bommala, R. Aluvalu, S. Mudrakola, High-Confidence Computing 3, 4 (2023) [Google Scholar]
- A. Al-Tayyan, K. Assaleh, T.Shanableh, Decision-level fusion for single-view gait recognition with various carrying and clothing conditions, Image and Vision Computing, 61, 54-69 (2017) [Google Scholar]
- Yang JC, Biometrics verification techniques combing with digital signature for multimodal biometrics payment system, In Proceedings of the IEEE international conference on management of e-commerce and e-government, 23-24 October 2010, Chengdu, China (2010) [Google Scholar]
- Liu, W. ; Li, W. ; Sun, L. ; Zhang, L. ; Chen, P, Finger vein recognition based on deep learning, In Proceedings of the 12th IEEE Conference on Industrial Electronics and Applications, 18–20 June2017, Siem Reap, Cambodia (2017) [Google Scholar]
- H. Bommala, Solid State Technology 64,2 (2021) [Google Scholar]
- M. Armbrust, I. Stoica, M. Zaharia, A. Fox, R. Griffith, A.D. Joseph, M. Zaharia, Communications of the ACM 53, 50 (2010) [CrossRef] [Google Scholar]
- I.A.T. Hashem, I. Yaqoob, N.B. Anuar, S. Mokhtar, A. Gani, S.U. Khan, Information Systems 47, 98 (2015) [Google Scholar]
- P.K. Sharma, R. Rani, International Journal of Information Management 43, 146 (2018) [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.