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A Robust Deep Features Enabled Touchless 3D-Fingerprint Classification System

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Abstract

The exponential rise in software computing and hardware technologies has broadened the horizon for different applications in decision making to make human life efficient. Among all the major demands, security systems have always been the dominant one to ensure authenticity of data, source or certain activity. Fingerprint technology has gained wide-spread attention for personalized data, resource or activity accesses authentication. Though, numerous methods have been developed for fingerprint detection and identification, the local input environment, data suitability, distortion and hardware dependency have been the challenge to yield optimal performance. On contrary, the possibilities of touchless 3D-fingerprint identification systems have attracted scientific communities due to ease of implementation, reduced dependency on local environment and sensing hardware. In this paper deep features-based Touchless 3D-Fingerprint Classification System is proposed. In this model a transfer deep-learning model AlexNet-CNN is used for deep feature extraction and classification, which obtains 4096 dimensional deep features. The proposed approach achieves a classification accuracy of 90.20%.

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References

  1. Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of fingerprint recognition. 2nd ed. London: Springer; 2009.

    Book  Google Scholar 

  2. Parziale G. Touchless fingerprinting technology. In: Ratha NK, Govindaraju V, editors. Advances in biometrics. London: Springer; 2008. p. 25–48.

    Chapter  Google Scholar 

  3. de Santos-Sierra A, Sánchez-Ávila C, del Pozo GB, Guerra-Casanova J. Unconstrained and contactless hand geometry biometrics. Sensors. 2011;11(11):10143–64.

    Article  Google Scholar 

  4. Han Y, Sun Z, Wang F, Tan T (2007) Palmprint Recognition Under Unconstrained Scenes. In: Yagi Y, Kang SB, Kweon IS, Zha H (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_1

  5. Malassiotis S, Aifanti N, Strintzis M. Personal authentication using 3-D finger geometry. IEEE Trans Inf Forensics Secur. 2006;1(1):12–21.

    Article  Google Scholar 

  6. Ma Z, Fang L, Duan J, Xie S, Wang Z. Personal identification based on finger vein and contour point clouds matching. In: 2016 IEEE international conference on mechatronics and automation. 2016. pp. 1983–1988. https://doi.org/10.1109/ICMA.2016.7558870

  7. De Marsico M, Nappi M, Riccio D, Wechsler H. Robust face recognition for uncontrolled pose and illumination changes. IEEE Trans Syst Man Cybern Syst. 2013;43(1):149–63.

    Article  Google Scholar 

  8. Donida Labati R, Genovese A, Piuri V, Scotti F. Iris segmentation: state of the art and innovative methods. In: Liu C, Mago V, editors. Cross disciplinary biometric systems (intelligent systems reference library), vol. 37. Berlin: Springer; 2012.

    Google Scholar 

  9. Rogez G, Rihan J, Guerrero J, Orrite C. Monocular 3-D gait tracking in surveillance scenes. IEEE Trans Cybern. 2014;44(6):894–909.

    Article  Google Scholar 

  10. Bustard J, Nixon M. Toward unconstrained ear recognition from two-dimensional images. IEEE Trans Syst Man Cybern A Syst Humans. 2010;40(3):486–94.

    Article  Google Scholar 

  11. Labati RD, Genovese A, Piuri V, Scotti F. Contactless fingerprint recognition: a neural approach for perspective and rotation effects reduction. In: 2013 IEEE symposium on computational intelligence in biometrics and identity management (CIBIM). 2013. pp. 22–30. https://doi.org/10.1109/CIBIM.2013.6607909.

  12. DonidaLabati R, Piuri V, Scotti F. Neural-based quality measurement of fingerprint images in contactless biometric systems. In: The 2010 international joint conference on neural networks (IJCNN), July 18–23, 2010. pp 1–8.

  13. Kang W, Wu Q. Pose-invariant hand shape recognition based on finger geometry. IEEE Trans Syst Man Cybern Syst. 2014;44(11):1510–21.

    Article  Google Scholar 

  14. AFIS and Biometrics Consulting. Fingerprint-on-the-Move. 2011; http://www.afisandbiometrics.com.

  15. Proenca H, Du E, Scharcanski J. Introduction to the special issue on unconstrained biometrics: advances and trends. Signal Image Video Process. 2011;5(4):399–400.

    Article  Google Scholar 

  16. Khan MMU, Sadi MS. An efficient approach to extract singular points for fingerprint recognition. In: 2012 7th international conference on electrical and computer engineering. 2012. pp 13–16. https://doi.org/10.1109/ICECE.2012.6471472.

  17. Jain AK, Prabhakar S, Hong L. A multichannel approach to fingerprint classification. IEEE Trans Pattern Anal Mach Intell. 1999;21(4):348–59.

    Article  Google Scholar 

  18. Ding S, Bian W, Liao H, Sun T, Xue Y. Combining Gabor filtering and classification dictionaries learning for fingerprint enhancement. IET Biometrics. 2017;6(6):438–47.

    Article  Google Scholar 

  19. Tan X, Bhanu B, Lin Y. Fingerprint classification based on learned features. IEEE Trans Syst Man Cybernet Part C (Appl Rev). 2005;35(3):287–300.

    Article  Google Scholar 

  20. Shah S, Sastry PS. Fingerprint classification using a feedback-based line detector. IEEE Trans Syst Man Cybernet Part B (Cybernetics). 2004;34(1):85–94.

    Article  Google Scholar 

  21. Halici U, Ongun G. Fingerprint classification through self-organizing feature maps modified to treat uncertainties. Proc IEEE. 1996;84(10):1497–512.

    Article  Google Scholar 

  22. Si X, Feng J, Zhou J, Luo Y. Detection and rectification of distorted fingerprints. IEEE Trans Pattern Anal Mach Intell. 2015;37(3):555–68.

    Article  Google Scholar 

  23. Mishra A, Khare N. A review on gender classification using association rule mining and classification based on fingerprints. In: 2015 fifth international conference on communication systems and network technologies. 2015. pp. 930–934. https://doi.org/10.1109/CSNT.2015.244.

  24. Satheesh Kumar P, Valarmathy S (2012) Development of a novel algo[1]rithm for SVMBDT ingerprint classiier based on clustering approach. In: 2012 international conference on advances in engineering, science and management (ICAESM 2012), Nagapattinam, Tamil Nadu, India, 30–31 March 2012, vol 4. 2012. IEEE.

  25. Deepika KC, Shivakumar G. Touchless 3D fingerprint classiication: a systematic survey. In: 2018 Third international IEEE conference on electrical, electronics, communication computer technologies and optimization techniques (ICEECCOT), GSSSIETW, Mysuru, Karnataka, India, December 14–15, 2018.

  26. Zhang Y, Shi D, Zhan X, Cao D, Zhu K, Li Z. Slim-ResCNN: a deep residual Convolutional Neural Network for fingerprint liveness detection. IEEE Access. 2019;7:91476–87.

    Article  Google Scholar 

  27. Yuan C, Xia Z, Jiang L, Cao Y, Jonathan Wu QM, Sun X. Fingerprint Liveness detection using an improved CNN with image scale equalization. IEEE Access. 2019;7:26953–66.

    Article  Google Scholar 

  28. Deepika KC, Shivakumar G. Touchless 3D fingerprint identiica[1]tion using SSIM template matching technique. Dogo Rangsang Res J (UGC Care Group I J) 2020;10(08). 13 August 2020. ISSN: 2347-7180.

  29. Tertychnyi P, Ozcinar C, Anbarjafari G. Low-quality fingerprint classification using deep neural network. IET Biometrics. 2018;7(6):550–6.

    Article  Google Scholar 

  30. Merchant K, Revay S, Stantchev G, Nousain B. Deep learning for RF device fingerprinting in cognitive communication networks. IEEE J Select Topics Signal Process. 2018;12(1):160–7.

    Article  Google Scholar 

  31. Wang R, C Han, T Guo. A novel fingerprint classification method based on deep learning. In:  2016 23rd International Conference on Pattern Recognition (ICPR). 2016. pp 931–36. https://doi.org/10.1109/ICPR.2016.7899755.  

  32. Donida Labati R, Genovese A, Piuri V, Scotti F. Quality measurement of unwrapped three-dimensional fingerprints: a neural networks approach. In: The 2012 International Joint Conference on Neural Networks (IJCNN). 2012. pp. 1–8. https://doi.org/10.1109/IJCNN.2012.6252519.

  33. Lin C, Kumar A. Contactless and partial 3D fingerprint recognition using multi-view deep representation. Pattern Recogn. 2018. https://doi.org/10.1016/j.patcog.2018.05.004.

    Article  Google Scholar 

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Acknowledgements

The Authors would like to thank the management, Principal and authorities of Malnad College of Engineering, Hassan for extending full support in carrying out this research work.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan, VTU, Belagavi, Karnataka, India

    K. C. Deepika

  2. Department of Electronics and Instrumentation Engineering, Malnad College of Engineering, Hassan, VTU, Belagavi, Karnataka, India

    G. Shivakumar

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  1. K. C. Deepika
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Correspondence to K. C. Deepika.

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This article is part of the topical collection “Data Science and Communication” guest edited by Kamesh Namudri, Naveen Chilamkurti, Sushma S J and S. Padmashree.

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Deepika, K.C., Shivakumar, G. A Robust Deep Features Enabled Touchless 3D-Fingerprint Classification System. SN COMPUT. SCI. 2, 263 (2021). https://doi.org/10.1007/s42979-021-00657-x

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