A HYBRID APPROACH FOR DEEP LEARNING BASED FINGER VEIN BIOMETRICS TEMPLATE SECURITY
Abstract
We are living in the today’s society, where we have fairly-enough storage capacity and processing
power, the only issue is with security. As, the technologies are evolving with faster rate, we
are tend to grow the use of electronic devices rapidly in todays’ society, it started to flow or leakage
of personal information around/across, which then leads to breach of this information. Now,
personal or identical verification is key problem is being crucial. So whatever traditional methods
we have for providing authentication or security those have proven inadequate to be unreliable
and do not provide strong security. Biometric template protection is one of the most important
issues in securing today’s biometric system. We have many algorithms which don’t give adequate
solution for the same. So we tried to give a method which will reach to the expectations more satisfactorily
and certainly to the extent required. In this paper we have discussed a hybrid method for
finger vein biometric recognition based on deep learning approach using BDD and fuzzy commitment
schemes. The proposed hybrid method consists of four parts, namely Finger vein feature
extraction, BDD-based secure template generation, Fuzzy commitment scheme and ML based
finger vein recognition and decision making. Thus it has four module and each module works efficiently
and gives accurate results on all databases.
References
Securing Deep Learning Based Edge Finger Vein Biometrics With Binary Decision Diagram,
IEEE Trans. on Industrial Informatics, July 2019, Vol. 15, No. 7, pp. 4244-4253.
2. Yi C. Feng, Pong C. Yuen, and Anil K. Jain. A Hybrid Approach for Generating Secure and
Discriminating Face Template, IEEE Tansactions on Information forensics and security,
March 2010, Vol. 5, No. 1, pp. 103-117.
3. Miura N., Nagasaka A., and Miyatake T. Feature extraction of fingervein patterns based on
repeated line tracking and its application to personal identification, Mach. Vision Appl., Oct.
2004, vol. 15, pp. 194-203.
4. Akers S.B. Binary decision diagrams, IEEE Trans. Comput. Jun. 1978, Vol. C-27, No. 6,
pp. 509-516.
5. Gupta P. and Gupta P. An accurate finger vein based verification system, Digit. Signal Process,
2015, Vol. 38, pp. 43-52.
6. Xi X., Yang L., and Yin Y. Learning discriminative binary codes for fingervein recognition,
Pattern Recognit, 2017, Vol. 66, pp. 26-33.
7. Yang L., Yang G., Yin Y., and Xi X. Finger vein recognition with anatomy structure analysis,
IEEE Trans. Circuits Syst. Video Technol., Aug. 2018, Vol. 28, No. 8, pp.1892-1905,.
8. Kauba C., Piciucco E., Maiorana E., Campisi P., and Uhl A. Advanced variants of feature
level fusion for finger vein recognition, Proc. Int. Conf. Biometrics Special Interest Group,
2016, pp. 195-206.
9. Liu C. and Kim Y.-H. An efficient finger-vein extraction algorithm based on random forest
regression with efficient local binary patterns, in Proc. IEEE Int. Conf. Image Process, 2016,
pp. 3141-3145.
10. Van H.T., Thai T.T., and Le T.H. Robust finger vein identification base on discriminant orientation
feature, in Proc. 7th Int. Conf. Knowl. Syst. Eng., 2015, pp. 348-353.
11. Banerjee A., Basu S., Basu S., and Nasipuri M. ARTeM: A new system for human authentication
using finger vein images, Multimedia Tools Appl., 2018, Vol. 77, pp. 5857-5884.
12. Radzi S.A., Hani M.K., and Bakhteri R. Finger-vein biometric identification using convolutional
neural network, Turkish J.Elect.Eng. Comput. Sci., 2016, Vol. 24, pp. 1863-1878.
13. Hong H.G., Lee M.B., and Park K.R. Convolutional neural network- based finger-vein recognition
using NIR image sensors, Sensors, 2017, Vol. 17, pp. 1297.
14. Qin H. and El-Yacoubi M.A. Deep representation-based feature extraction and recovering for
finger-vein verification, IEEE Trans. Inf. Forensics Security, Aug. 2017, Vol. 12, No. 8,
pp. 1816-1829.
15. Das R., Piciucco E., Maiorana E., and Campisi P. Convolutional neural network for fingervein-
based biometric identification, IEEE Trans. Inf. Forensics Secur., Feb. 2019, Vol. 14,
No. 2, pp. 360-373.
16. Fang Y., Wu Q., and Kang W. A novel finger vein verification system based on two-stream
convolutional network learning, Neurocomputing, 2018, Vol. 290, pp. 100-107.
17. Huang G.-B., Zhu Q.-Y., and Siew C.-K. Extreme learning machine: theory and applications,
Neurocomputing, 2006, Vol. 70, pp. 489-501.
18. Xie S.J., Yoon S., Yang J., Lu Y., Park D.S., and Zhou B. Feature component-based extreme
learning machines for finger vein recognition, Cogn. Comput., 2014, Vol. 6, pp. 446-461.
19. Khalil-Han M. and Eng P.C. FPGA-Based embedded system implementation of finger vein
biometrics, IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010), October
3-5, 2010, Penang Malaysia.
20. Ayappan G. and Shankar A. Finger Vein biometric Authentication System, International Journal
of Trend in Research and Development, April 2017, Vol. 4 (2), pp. 51-53.
21. Yang W., Hu J., and Wang S. A finger-vein based cancellable bio- crypto system, in Proc. Int.
Conf. Netw. Syst. Secur., 2013, pp. 784-790.
22. Yang W., Wang S., Hu J., Zheng G., and Valli C. A fingerprint and finger-vein based cancelable
multi-biometric system, Pattern Recognit., 2018, Vol. 78, pp. 242-251.
23. Yang W., Hu J., Wang S., and Yang J. Cancelable fingerprint templates with Delaunay triangle-
based local structures, in Proc. Cyberspace Safety Secur.: 5th Int. Symp., 2013, pp. 81-91.
24. Vitomirand S., Nikola P. The complete Gabor-Fisher classifier for robust face recognition,
EURASIP J.Advances Signal Process., 2010, Vol. 2010, Art. no. 31.
25. Jin A.T.B., Ling D.N.C., and Goh A. Bio hashing: Two factor authentication featuring finger
print data and tokenised random number, Pattern Recognit., 2004, Vol. 37, pp. 2245-2255.
26. Kasun L.L.C., Zhou H., Huang G.-B., and Vong C.M. Representational learning with extreme
learning machine for big data, IEEE Intell. Syst., Dec. 2013, Vol. 28, No. 6, pp. 31-34.
