Palm vein recognition based on convolution neural network
DOI:
https://doi.org/10.29304/jqcm.2021.13.3.822Keywords:
Palm Vein, Convolution Neural Network, AlexNet, Biometric Authentication, deep learningAbstract
This paper presents a new validation method using a convolutional neural network for palm vein recognition. Unlike fingerprint and face. Vein patterns are endogenous biometric features that do not change over time and that make them difficult to identify and replicate in people. The proposed paper aims to provide a new way to identify people through their veins. This paper used the CASIA dataset, which consists of several wavelengths, in this research used the 850nm wavelength, which is clear in the veins, In addition, we divided the data into 3 cases. The first case is when the training and testing ratio is 50/50, the second case when it is 70/30, and the last case when it is 90/10. Obtained an accuracy of 98% in the case 90/10. In addition, to the proposed network, and used a well-known global network, the AlexNet network, where did the same work on it to compare the results of our proposed network with it. As proposed network outperformed it in terms of accuracy and speed, where the accuracy was 96% in the case 90/10.
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References
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[4] Z. M. Noh, A. R. Ramli, M. Hanafi, and M. I. Saripan, “Review on a Palm Vein Infrared Image Acquisition System,” no. December, pp. 16–17, 2013.
[5] B. Prommegger and A. Uhl, “Rotation Invariant Finger Vein Recognition,” 2019 IEEE 10th Int. Conf. Biometrics Theory, Appl. Syst. BTAS 2019, no. 61173069, pp. 151–156, 2019, doi: 10.1109/BTAS46853.2019.9185982.
[6] X. Zhu and D. Huang, “Hand Dorsal Vein Recognition Based on Hierarchically,” no. 2, pp. 157–164, 2012.
[7] J. M. Cross and C. L. Smith, “Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification,” IEEE Annu. Int. Carnahan Conf. Secur. Technol. Proc., no. v, pp. 20–35, 1995, doi: 10.1109/ccst.1995.524729.
[8] L. Wang and G. Leedham, “Near- and far- infrared imaging for vein pattern biometrics,” Proc. - IEEE Int. Conf. Video Signal Based Surveill. 2006, AVSS 2006, pp. 52–57, 2006, doi: 10.1109/AVSS.2006.80.
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[10] N. G. Peppes and N. G. Peppes, “Ship Detection in Satellite Images With Deep Learning and a Pythonic Interface on a Hadoop HDFS Platform MASTER THESIS Ship Detection in Satellite Images With Deep Learning and a Pythonic Interface on a Hadoop HDFS Platform MASTER THESIS,” 2018.
[11] Nada B.jarah, “Deep Learning in Wireless Sensor network,” vol. 13, no. 1, pp. 11–17, 2019.
[12] E. Calli, “Faster Convolutional Neural Networks,” p. 11, 2017, [Online]. Available: https://theses.ubn.ru.nl/bitstream/handle/123456789/5233/Çalli%2C_E._1.pdf?sequence=1.
[13] H. Mohsin and H. Hasan, “Convolution Neural Network with Dual Tree Complex Wavelet Transform Preprocessor for Universal Image Steganalysis,” vol. 11, no. 3, pp. 43–58, 2019.
[14] H. Mohsin Ahmed and H. Hasan Mahmoud, “Convolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study,” J. Al-Qadisiyah Comput. Sci. Math., vol. 11, no. 2, pp. 53–64, 2019, doi: 10.29304/jqcm.2019.11.2.573.
[15] H. I. Abdulrazzaq, “Robust Authorization Based on Multi Biometric Techniques,” 2018.
[16] T. M. Mezza, N. R. Armstrong, G. W. Ritter, J. P. Iafalice, and M. E. Kenney, “Electrochemical studies on stacked-ring silicon phthalocyanines,” J. Electroanal. Chem., vol. 137, no. 2, pp. 227–237, 1982, doi: 10.1016/0022-0728(82)80038-7.
[17] L. M. Q. De Santana, R. M. Santos, L. N. Matos, and H. T. Macedo, “Deep Neural Networks for Acoustic Modeling in the Presence of Noise,” IEEE Lat. Am. Trans., vol. 16, no. 3, pp. 918–925, 2018, doi: 10.1109/TLA.2018.8358674.
[18] A. S. Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, and T. A. M. Nagem, “A multi-biometric iris recognition system based on a deep learning approach,” Pattern Anal. Appl., vol. 21, no. 3, pp. 783–802, 2018, doi: 10.1007/s10044-017-0656-1.
[19] M. Thoma, “Analysis and Optimization of Convolutional Neural Network Architectures,” Karlsruhe Institute of Technology, MSC thesis, ”2017.
[20] Laurent, César, et al. "Batch normalized recurrent neural networks." 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE,”2016.
[21] Chen, Jun-Cheng, Vishal M. Patel, and Rama Chellappa. "Unconstrained face verification using deep CNN features." 2016 IEEE winter conference on applications of computer vision (WACV). IEEE,”2016.
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Published
2021-07-25
How to Cite
Al-jaberi, A. S., & Al-juboori, A. M. (2021). Palm vein recognition based on convolution neural network. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(3), Comp Page 1 – 14. https://doi.org/10.29304/jqcm.2021.13.3.822
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Computer Articles
