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Dorsal Hand Veins Based Biometric Identification System Using Deep Learning

Year 2021, Volume: 14 Issue: 1, 1 - 15, 31.03.2021
https://doi.org/10.18185/erzifbed.848004

Abstract

Identification systems have become biometric-based, especially with the increase in the performance rates of machine learning methods. Biometric identification systems offer a high level of security by using reliable, difficult-to-change parameters. In this thesis, a biometric identification system is proposed using dorsal hand vein patterns. The relevant system has been tested on the sample dataset in the literature. The number of data were increased by adding noisy data to the data set used. The classification was made on the preprocessed images using SVM, ANN, LDA + KNN, and CNN methods. It has been determined that the highest identification accuracy is achieved when CNN is used, and CNN method provides higher performance compared to other methods. With the proposed identification system, after multiple runs, an average accuracy of 99.64% is achieved with the CNN machine learning method.

References

  • Alasadi, A. H. H., & Dawood, M. H. (2017). Dorsal hand-vein images recognition system based on grey level co-occurrence matrix and Tamura features. International Journal of Applied Pattern Recognition, 4(3), 207. https://doi.org/10.1504/IJAPR.2017.086586
  • Badawi, A. M. (2006). Hand vein biometric verification prototype: A testing performance and patterns similarity. Proceedings of the 2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV’06, 1, 3–9.
  • Bagosi, T., & Baruch, Z. (2011). Indoor localization by WiFi. Proceedings - 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing, ICCP 2011, 449–452. https://doi.org/10.1109/ICCP.2011.6047914
  • Benhar, H., Idri, A., & Fernández-Alemán, J. L. (2020). Data preprocessing for heart disease classification: A systematic literature review. Computer Methods and Programs in Biomedicine, 195, 105635. https://doi.org/10.1016/j.cmpb.2020.105635
  • Charte, D., Charte, F., García, S., del Jesus, M. J., & Herrera, F. (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines.
  • Information Fusion, 44(December 2017), 78–96. https://doi.org/10.1016/j.inffus.2017.12.007
  • Cross, J. M., & Smith, C. L. (1995). Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification. IEEE Annual International Carnahan Conference on Security Technology, Proceedings, (vi), 20–35. https://doi.org/10.1109/ccst.1995.524729
  • Ganegedara, T. (n.d.). Intuitive Guide to Convolution Neural Networks - Towards Data Science. Retrieved June 28, 2020, from https://towardsdatascience.com/light-on-math-machine-learning-intuitive-guide-to-convolution-neural-networks-e3f054dd5daa
  • Jain, A. K. (2007). Biometric recognition. Nature (Vol. 449). https://doi.org/10.1038/449038a
  • Li, J., Feng, J., & Kuo, C. C. J. (2018). Deep convolutional neural network for latent fingerprint enhancement. Signal Processing: Image Communication, 60, 52–63. https://doi.org/10.1016/j.image.2017.08.010
  • Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J.-M. (Eds.). (2007). Wavelets and their Applications. London, UK: ISTE. https://doi.org/10.1002/9780470612491
  • Patel, N., & Mishra, A. (2015). Automated Leukaemia Detection Using Microscopic Images. In Second International Symposium on Computer Vision and the Internet (Vol. 58, pp. 635–642). https://doi.org/10.1016/j.procs.2015.08.082
  • Shahin, M., Badawi, A., & Kamel, M. (2008). Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns. World Academy of Science, Engineering and Technology, 2(1), 756–763.
  • TTP, T., N. Pham, G., Park, J.-H., Moon, K.-S., Lee, S.-H., & Kwon, K.-R. (2017). Acute Leukemia Classification Using Convolution Neural Network in Clinical Decision Support System. Computer Science & Information Technology (CS & IT), 7, 49–53. https://doi.org/10.5121/csit.2017.71305
  • Turgut, Z., Üstebay, S., Zeynep Gürkaş Aydın, G., & Sertbaş, A. (2019). Deep learning in indoor localization using WiFi. Lecture Notes in Electrical Engineering (Vol. 504). https://doi.org/10.1007/978-981-13-0408-8_9
  • Varastehpour, S. (2020). Visualising Vein Pattern Based on Sparse Auto-Encoder Algorithm. Unitec Institute of Technology.
  • Veropoulos, K., Campbell, C., Cristianini, N., & Others. (1999). Controlling the sensitivity of support vector machines. Proceedings of the International Joint Conference on Artificial Intelligence, 55–60. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.7895%5Cnhttp://seis.bris.ac.uk/~enicgc/pubs/1999/ijcai_ss.pdf
  • Vijayanand, R., Devaraj, D., & Kannapiran, B. (2018). Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Computers and Security, 77, 304–314. https://doi.org/10.1016/j.cose.2018.04.010
  • Wang, Y., Zheng, X., & Wang, C. (2016). Dorsal Hand Vein Recognition Across Different Devices (pp. 307–316). https://doi.org/10.1007/978-3-319-46654-5_34

Dorsal Hand Veins Based Biometric Identification System Using Deep Learning

Year 2021, Volume: 14 Issue: 1, 1 - 15, 31.03.2021
https://doi.org/10.18185/erzifbed.848004

Abstract

Identification systems have become biometric based, especially with the increase in the performance rates of machine learning methods. Biometric identification systems offer a high level of security by using reliable, difficult-to-change parameters. In this study, a biometric identification system is proposed using dorsal hand vein patterns. The relevant system has been tested on the sample dataset in the literature. The number of data were increased by adding noisy data to the data set used. Classification was made on the preprocessed images using SVM, ANN, LDA + KNN, and CNN methods. It has been determined that the highest identification accuracy is achieved when CNN is used, and CNN method provides higher performance compared to other methods. With the proposed identification system, after multiple runs, an average accuracy of 99.64% is achieved with the CNN machine learning method.

References

  • Alasadi, A. H. H., & Dawood, M. H. (2017). Dorsal hand-vein images recognition system based on grey level co-occurrence matrix and Tamura features. International Journal of Applied Pattern Recognition, 4(3), 207. https://doi.org/10.1504/IJAPR.2017.086586
  • Badawi, A. M. (2006). Hand vein biometric verification prototype: A testing performance and patterns similarity. Proceedings of the 2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV’06, 1, 3–9.
  • Bagosi, T., & Baruch, Z. (2011). Indoor localization by WiFi. Proceedings - 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing, ICCP 2011, 449–452. https://doi.org/10.1109/ICCP.2011.6047914
  • Benhar, H., Idri, A., & Fernández-Alemán, J. L. (2020). Data preprocessing for heart disease classification: A systematic literature review. Computer Methods and Programs in Biomedicine, 195, 105635. https://doi.org/10.1016/j.cmpb.2020.105635
  • Charte, D., Charte, F., García, S., del Jesus, M. J., & Herrera, F. (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines.
  • Information Fusion, 44(December 2017), 78–96. https://doi.org/10.1016/j.inffus.2017.12.007
  • Cross, J. M., & Smith, C. L. (1995). Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification. IEEE Annual International Carnahan Conference on Security Technology, Proceedings, (vi), 20–35. https://doi.org/10.1109/ccst.1995.524729
  • Ganegedara, T. (n.d.). Intuitive Guide to Convolution Neural Networks - Towards Data Science. Retrieved June 28, 2020, from https://towardsdatascience.com/light-on-math-machine-learning-intuitive-guide-to-convolution-neural-networks-e3f054dd5daa
  • Jain, A. K. (2007). Biometric recognition. Nature (Vol. 449). https://doi.org/10.1038/449038a
  • Li, J., Feng, J., & Kuo, C. C. J. (2018). Deep convolutional neural network for latent fingerprint enhancement. Signal Processing: Image Communication, 60, 52–63. https://doi.org/10.1016/j.image.2017.08.010
  • Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J.-M. (Eds.). (2007). Wavelets and their Applications. London, UK: ISTE. https://doi.org/10.1002/9780470612491
  • Patel, N., & Mishra, A. (2015). Automated Leukaemia Detection Using Microscopic Images. In Second International Symposium on Computer Vision and the Internet (Vol. 58, pp. 635–642). https://doi.org/10.1016/j.procs.2015.08.082
  • Shahin, M., Badawi, A., & Kamel, M. (2008). Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns. World Academy of Science, Engineering and Technology, 2(1), 756–763.
  • TTP, T., N. Pham, G., Park, J.-H., Moon, K.-S., Lee, S.-H., & Kwon, K.-R. (2017). Acute Leukemia Classification Using Convolution Neural Network in Clinical Decision Support System. Computer Science & Information Technology (CS & IT), 7, 49–53. https://doi.org/10.5121/csit.2017.71305
  • Turgut, Z., Üstebay, S., Zeynep Gürkaş Aydın, G., & Sertbaş, A. (2019). Deep learning in indoor localization using WiFi. Lecture Notes in Electrical Engineering (Vol. 504). https://doi.org/10.1007/978-981-13-0408-8_9
  • Varastehpour, S. (2020). Visualising Vein Pattern Based on Sparse Auto-Encoder Algorithm. Unitec Institute of Technology.
  • Veropoulos, K., Campbell, C., Cristianini, N., & Others. (1999). Controlling the sensitivity of support vector machines. Proceedings of the International Joint Conference on Artificial Intelligence, 55–60. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.7895%5Cnhttp://seis.bris.ac.uk/~enicgc/pubs/1999/ijcai_ss.pdf
  • Vijayanand, R., Devaraj, D., & Kannapiran, B. (2018). Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Computers and Security, 77, 304–314. https://doi.org/10.1016/j.cose.2018.04.010
  • Wang, Y., Zheng, X., & Wang, C. (2016). Dorsal Hand Vein Recognition Across Different Devices (pp. 307–316). https://doi.org/10.1007/978-3-319-46654-5_34
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Masoumeh Zehtab Nayebi This is me 0000-0002-0288-6461

Zeynep Turgut 0000-0002-9416-609X

Publication Date March 31, 2021
Published in Issue Year 2021 Volume: 14 Issue: 1

Cite

APA Zehtab Nayebi, M., & Turgut, Z. (2021). Dorsal Hand Veins Based Biometric Identification System Using Deep Learning. Erzincan University Journal of Science and Technology, 14(1), 1-15. https://doi.org/10.18185/erzifbed.848004