Contactless Fingerprint Recognition Based on Machine Learning and Neural Network
DOI:
https://doi.org/10.18486/ijcsnt/10.2.136Keywords:
Contactless Fingerprint, Biometrics, Convolutional Neural Network, Deep Learning, Deep Hashing, Machine learningAbstract
Fingerprints are something which is greatly examined biometry features. Particularly, based only through contact fingerprints identification systems are leading because of their powerfully built, flexibility, and wide research in the particular domain. But these type of systems has some issues like hygiene, and detector degradation because of the regular physical touch. In this project, we present a process for creating an amazing system of identification of fingerprints that catches ridges images through a space utilising a picture detector within proper conditions. That caught ridges pictures were moved forward to get global and local components. Particularly, (CNN) is developed in order to remove worldwide components through provided ridges image.
References
A. A. Moenssens, Fingerprint Techniques, 1st ed. Chilton Book Company, Philadelphia, 1971.
D. Maltoni, D. Maio, A. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, 2nd ed. Springer Science & Business Media, 2009. DOI: https://doi.org/10.1007/978-1-84882-254-2
A. Jain, L. Hong, and R. Bolle, “On-line fingerprint verification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 302–314, Apr. 1997. DOI: https://doi.org/10.1109/34.587996
V. Piuri and F. Scotti, “Fingerprint biometrics via low-cost sensors and webcams,” in 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems, Sep. 2008, pp. 1–6. DOI: https://doi.org/10.1109/BTAS.2008.4699379
R. D. Labati, A. Genovese, V. Piuri, and F. Scotti, “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), Apr. 2013, pp. 22–30. DOI: https://doi.org/10.1109/CIBIM.2013.6607909
B. Y. Hiew, A. B. J. Teoh, and D. C. L. Ngo, “Automatic digital camera based fingerprint image preprocessing,” in International Conference on Computer Graphics, Imaging and Visualisation (CGIV’06), July 2006, pp. 182–189. DOI: https://doi.org/10.1109/CGIV.2006.27
B. Y. Hiew, A. B. J. Teoh, and Y. H. Pang, “Digital camera based fingerprint recognition,” in 2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, May 2007, pp. 676–681. DOI: https://doi.org/10.1109/ICTMICC.2007.4448572
B. Hiew, A. B. Teoh, and D. C. Ngo, “Preprocessing of fingerprint images captured with a digital camera,” in 2006 9th International Conference on Control, Automation, Robotics and Vision, Dec. 2006, pp. 1–6. DOI: https://doi.org/10.1109/ICARCV.2006.345189
C. Lin and A. Kumar, “A cnn-based framework for comparison of contactless to contact-based fingerprints,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 3, pp. 662–676, Mar. 2019. DOI: https://doi.org/10.1109/TIFS.2018.2854765
G. K. O. Michael, T. Connie, and A. B. J. Teoh, “A contactless biometric system using multiple hand features,” Journal of Visual Communication and Image Representation, vol. 23, no. 7, pp. 1068–1084, Oct. 2012. DOI: https://doi.org/10.1016/j.jvcir.2012.07.004
A. Kumar, “Toward pose invariant and completely contactless finger knuckle recognition,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 1, no. 3, pp. 201–209, July 2019. DOI: https://doi.org/10.1109/TBIOM.2019.2928868
F. Zhang, S. Xin, and J. Feng, “Combining global and minutia deep features for partial high-resolution fingerprint matching,” Pattern Recognition Letters, vol. 119, pp. 139–147, 2019, Deep Learning for Pattern Recognition. DOI: https://doi.org/10.1016/j.patrec.2017.09.014
E. Davies, Computer and Machine Vision: Theory, Algorithms and Practicalities, 4th ed. Academic Press, 2012.
C. I. Watson, M. D. Garris, E. Tabassi, C. L. Wilson, R. M. Mccabe, S. Janet, and K. Ko, “User’s guide to NIST biometric image software (NBIS),” 2007. DOI: https://doi.org/10.6028/NIST.IR.7392
T. Guo, J. Dong, H. Li, and Y. Gao, “Simple convolutional neural network on image classification,” in 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Mar. 2017, pp. 721–724. DOI: https://doi.org/10.1109/ICBDA.2017.8078730
N. Jmour, S. Zayen, and A. Abdelkrim, “Convolutional neural networks for image classification,” in 2018 International Conference on Advanced Systems and Electric Technologies (IC ASET), 2018, pp. 397–402. DOI: https://doi.org/10.1109/ASET.2018.8379889
Sharma, V. Jain, and A. Mishra, “An analysis of convolutional neural networks for image classification,” Procedia Computer Science, vol. 132, pp. 377–384, 2018, International Conference on Computational Intelligence and Data Science. DOI: https://doi.org/10.1016/j.procs.2018.05.198
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Gururaj Surampali, Padmanjali A Hagargi, Hemavathi Patil, Rajashekar Gaithode
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
