Deep learning based person biometric identification

  • 1 Department of Computer Science and Robotics Ufa State Aviation Technical University Ufa, Russia
  • 2 Department of Applied Informatics, M.Akmullah named after Bashkir State Pedagogical University, Ufa, Russia

Abstract

The article is about the biometric person identification using Deep Learning methods. The issues related to the preprocessong of the signal, the extracting of biometric features and classification are considered. The implementation of the LSTM Deep Learning model, which is a kind of Recurrent neural network, is discussed. Factors affecting the accuracy of biometric identification are being studied

References

  1. Zhang X., Yao L., Chen K.,Wang X., Sheng Q.Z., Gu T. DeepKey: An EEG and Gait Based Dual-Authentication System. ACM J. Comput. Cult. Herit., Vol. 9, No. 4, Article 39. Publication date: March 2017.
  2. Tan B., Schuckers S. Spoofing Protection for Fingerprint Scanner by Fusing Ridge Signal and Valley Noise. https://www.clarkson.edu/sites/default/files/2017-11/spoofingprotection.pdf
  3. Bogdanov M.R., Dumchikov A.A, Kartak V.M., Fabarisova A.I. Optimizing Factors Influencing on Accuracy of Biometrical Cardiometry. OPTA-SCL 2018, Omsk, Russia, published at http://ceurws.org.
  4. Bogdanov M.R, Kartak V.M., Dumchikov A.A., Fabarisova A.I. Factors Influencing Accuracy of Biometrical Personal Identification Based on Cardiograms. DOI: 10.1134/S1054661818030033. ISSN 1054-6618, Pattern Recognition and Image Analysis, 2018, Vol. 28, No. 3, pp. 421–426. © Pleiades Publishing, Ltd., 2018.
  5. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215- e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13).
  6. Nemirko A.P., Lugovaya T.S. Biometric human identification based on electrocardiogram. Proc. XII-th Russian Conference on Mathematical Methods of Pattern Recognition, Moscow, MAKS Press, 2005, pp. 387-390. ISBN 5-317-01445-X.
  7. Ognian Boumbarov, Yuliyan Velchev, Krasimir Tonchev and Igor Paliy (2011). Face and ECG Based Multi-Modal Biometric Authentication, Advanced Biometric Technologies, Dr. Girija Chetty (Ed.), ISBN: 978- 953-307-487-0, InTech, Available from: http://www.intechopen.com/books/advanced-biometrictechnologies/face-and-ecg-based-multi-modal-biometric-authentication
  8. Manjunathswamy B.E., Abhishek A.M, Thriveni J, Venugopal K R, Patnaik L.M. Multimodal Biometric Authentication using ECG and Fingerprint. International Journal of Computer Applications (0975 – 8887). Volume 111 – No 13, February 2015.
  9. Gawande P.S., Ladhake S.A. Artificial Neural Network based Electrocardiogram Classification for Biometric Authentication. International Journal of Computer Applications (0975 – 8887). Volume 109 – No. 2, January 2015.
  10. Page A., Kulkarni A., Mohsenin T. Utilizing Deep Neural Nets for an Embedded ECG-based Biometric Authentication System. 978-1-4799- 7234-0/15/$31.00 ©2015 IEEE.
  11. Tantawi M., Revett K., Salem Abd-B., Tolba M. ECG based Biometric Recognition using Wavelets and RBF Neural Network. Recent Advances in Information Science. ISBN: 978-960-474-304-9.
  12. Kim Ho J., Lim J.S. Study on a Biometric Authentication Model based on ECG using a Fuzzy Neural Network. 4th International Conference on Advanced Engineering and Technology (4th ICAET). IOP Conf. Series: Materials Science and Engineering 317 (2018) 012030 doi:10.1088/1757-899X/317/1/012030
  13. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215- e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13).
  14. Amit Shekhar. Understanding the Recurrent Neural Network. https://medium.com/mindorks/understanding-the-recurrent-neuralnetwork-44d593f112a2
  15. Nikolenko S. Deep learning. https://logic.pdmi.ras.ru/~sergey/teaching/mlhse17/35-dl2-backprop.pdf

Article full text

Download PDF