Survey on finger-vein segmentation and authentication

  • Authors

    • Shwetambari Kharabe
    • C. Nalini
    https://doi.org/10.14419/ijet.v7i1.2.8962

    Received date: December 28, 2017

    Accepted date: December 28, 2017

    Published date: December 28, 2017

  • Abstract

    Exploding growth in the field of electronic information technology, the finger vein authentication technique plays a vibrant role for personal identification and verification. In recent era, this technique is gaining popularity, as it provides a high security and convenience approach for personal authentication. Vein biometrics is an emerging methodologycomparing to other systems, due to its strengths of low forgery risk, aliveness detection and stableness over long period of time. Literatures published based on different techniques used forand authentication process are described and evaluated in this paper. These processes hadgained an outstanding promise in variety of applications and much attention among researchers to provide combine accuracy, universality and cost efficiency. This paper in brief, reviews various approaches used for finger vein segmentation and feature extraction. The reviews are based on finger vein basic principles, image acquisition methodology, pre-processing functions, segmentation, feature extraction,classification, matching and identification procedures, which are analyzed scientifically, thoroughly and comprehensively.Based on the analysis, the ideal process and procedure is identified, which will be an idyllic solution for finger vein authentication.

  • References

    1. P. Gupta, "An accurate finger vein based verification system," Digi-tal Signal Processing, vol. 38, pp. 43-52, 3// 2015.
    2. Y. Jinfeng and L. Xu, "Efficient Finger Vein Localization and Recognition," in Pattern Recognition (ICPR), 2010 20th Interna-tional Conference on, 2010, pp. 1148-1151.
    3. David Mulyono, “A study of finger vein biometric for personal identification” IEEE Proceedings of the Tnternational Symposium Biometrics and Security technologies, pages 134-141, 2008.
    4. Zhi Liu and Shangling Song, “An embedded real-time finger-vein recognition system for mobile devices” IEEE Transactions on Con-sumer Electronics, 58(2), 2012. https://doi.org/10.1109/TCE.2012.6227456.
    5. F. C. Monteiro and A. Campilho, "Watershed framework to region-based image segmentation," in Proc. International Conference on Pattern Recognition, ICPR 19th, pp. 1-4, 2008. https://doi.org/10.1109/ICPR.2008.4761587.
    6. M. Hameed, M. Sharif, M. Raza, S. W. Haider, and M. Iqbal, "Framework for the comparison of classifiers for medical image segmentation with transform and moment based features," Research Journal of Recent Sciences, vol. 2277, p. 2502, 2012.
    7. R. Patil and K. Jondhale, "Edge based technique to estimate num-ber of clusters in k-means color image segmentation," in Proc. 3rd IEEE International Conference on Computer Science and Infor-mation Technology (ICCSIT), pp. 117-121, 2010. https://doi.org/10.1109/ICCSIT.2010.5563647.
    8. W. Cui and Y. Zhang, "Graph based multispectral high resolution image segmentation," in Proc. International Conference on Multi-media Technology (ICMT), pp. 1-5, 2010.
    9. A. Fabijanska, "Variance filter for edge detection and edge-based image segmentation," in Proc. International Conference on Perspec-tive Technologies and Methods in MEMS Design (MEMSTECH), pp. 151-154, 2011.
    10. M. J. Islam, S. Basalamah, M. Ahmadi, and M. A. S. hmed, "Cap-sule image segmentation in pharmaceutical applications using edge-based techniques," IEEE International Conference on Elec-tro/Information Technology (EIT), pp. 1-5, 2011.
    11. D. Barbosa, T. Dietenbeck, J. Schaerer, J. D'hooge, D. Friboulet, and O. Bernard, "B-spline explicit active surfaces: An efficient framework for real-time 3-D region-based segmentation," IEEE Transactions on Image Processing, vol. 21, pp. 241-251, 2012. https://doi.org/10.1109/TIP.2011.2161484.
    12. G. Chen, T. Hu, X. Guo, and X. Meng, "A fast region-based image segmentation based on least square method," in Proc. IEEE Interna-tional Conference on Systems, Man and Cybernetics, SMC, pp. 972-977, 2009. https://doi.org/10.1109/ICSMC.2009.5346073.
    13. Z. Hua, Y. Li, and J. Li, "Image segmentation algorithm based on improved visual attention model and region growing," in Proc. 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), pp. 1-4, 2010. https://doi.org/10.1109/WICOM.2010.5601008.
    14. T. Mei, C. Zheng, and S. Zhong, "Hierarchical region based Markov random field for image segmentation," in Proc. International Con-ference on Remote Sensing, Environment and Transportation Engi-neering (RSETE), pp. 381-384, 2011.
    15. N. Miura, A. Nagasaka, and T. Miyatake, “Feature extraction of finger vein patterns based on repeated line tracking and its applica-tion to personal identification”, Machine Vision Application, vol. 15, no.4, pp.194–203, 2004. https://doi.org/10.1007/s00138-004-0149-2.
    16. M.Subramani, “Highly secure and reliable user identification based on finger vein patterns” Double Blind Peer Reviewed International Research Journal, 11, 2011.
    17. D. Wang , J. Li, and G. Memik, “User identification based on finger vein patterns for consumer electronics devices”, IEEE Transactions on Consumer Electronics, vol. 56, no. 2, pp. 799-804, 2010. https://doi.org/10.1109/TCE.2010.5506004.
    18. V.Ramya and P.Vijaykumar, “A novel design of finger vein recog-nition for personal authentication and vehicle security” Journal of Theoretical and Applied Information Technology, 65, 2014.
    19. Yang, G., Xi, X., Yin, Y.: Finger Vein Recognition Based on a Per-sonalized Best Bit Map.Sensors 12, 1738–1757 (2012) https://doi.org/10.3390/s120201738.
    20. M. Khalil-Hani and P.C. Eng., “FPGA-based embedded system implemention of finger vein biometrics” IEEE Symposium on In-dustrial Electronics and Applications, 2010.
    21. M. Vlachos, E. Dermatas: “A Region Growing Method Based on Statistical Attributes of Infrared Images for Finger Vein Pattern Ex-traction”,Biomedical Engineering Research,March. 2013, Vol. 2 Iss. 1, PP. 1-15.
    22. Wang Y, T. Liu and J. Jiang 2008b, “A Multi resolution waveletal-gorithm for hand vein pattern recognition”, Chinese Optics Lett., 6:657-660. https://doi.org/10.3788/COL20080609.0657.
    23. Kono, M, H.Ueki and S.Umemura (2002), “Near Infra-red Finger-veinPattern for Personal Identification”, Applied Optics, 41:7429-7436. https://doi.org/10.1364/AO.41.007429.
    24. C.Nalini, Shwtambari Kharabe,” A Comparative study on different techniques used for finger –vein authentication”,International Jour-nal of Pure and Applied Mathematics,Vol-16,No.8,2017,pp 327-333.

  • Downloads

  • How to Cite

    Kharabe, S., & Nalini, C. (2017). Survey on finger-vein segmentation and authentication. International Journal of Engineering and Technology, 7(1.2), 9-14. https://doi.org/10.14419/ijet.v7i1.2.8962