Fingerprint Authenticity Classification Algorithm based-on Distance of Minutiae using Convolutional Neural Network

Hariyanto Hariyanto, Sarifuddin Madenda, Sunny Arief Sudiro, Tubagus Maulana Kusuma

Abstract


Fingerprint identification systems are vulnerable to attempted authentication fraud by creating fake fingerprints that mimic the live. This paper proposes method to detect whether a fingerprint is live fingerprint or fake fingerprint using Convolutional Neural Network (CNN). We construct a features database of distances among minutiaes of fingerprints, where the distance calculation is based-on Euclidean Distance. Furthermore, the distance features database that has been constructed is classified using the CNN. CNN is a deep learning method designed for machine learning processes so that computers recognize objects in an image and this method has capability classifying an object. The numerical results have shown that the best accuracy achieves 99.38% when the learning rate is 0.001 with the epoch of 100.

Keywords


Fingerprint; Live; Fake; CNN;

Full Text:

PDF

References


Maltoni, Davide., “Handbook of Fingerprint Recognitioni”. 2nd Edition, Springer: London, 2009.

Digital Persona inc., “Guide to Fingerprint Recognition”, White Paper, from http://dl.fecpos.com/CustomerService/Peripheral/Finger_Print/Manual/Guide to Fingerprint Recognition.pdf (Diakses 07 Mei 2021).

Bhattacharya S., Mali K. “Fingerprint Recognition Using Minutiae Extraction Method”, Proc. of International Conferance on Emerging Technologies (ICET-2011): International Journal of Electrical Engineering and Embedded Systems, 2011. Pages 0975-4830.

Martin S. K., D. Narain Ponraj, Winston J, Yaspy J C, Jeba E.D, Clara A., “Authentication of Biometric System using Fingerprint Recognition with Euclidean Distance and Neural Network Classifier”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, 2019, Volume-8 Issue-4.

R. F. Nogueira, R. de Alencar Lotufo, and R. C. Machado, “Fingerprint Liveness Detection using Convolutional Networks,” Ieee Trans. Inf. Forensics Secur., vol. 11, no. 6, 2016, pp. 1206–1213.

Hindi, Amjad, Dwairi M. O., Alqadi Z. “Analysis of Fingerprint Minutiae to form Finger Identifier”, IJCSMC, Vol.9, Issue. 2, 2020.

AlQadi Z.A., Eltous Y., Abuzalata M., Qaryouti G.M. “Detecting and Counting Minutiae in Human Fingerprint”. Open Science Journal 5(1), 2020.

Wang C., Li K., Wu Z., Zhao Q., “A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy”. In: Yang J., Yang J., Sun Z., Shan S., Zheng W., Feng J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science, vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_29, 2015.

Jang, HU, Choi, HY, Kim, D ., Son, J., Lee, HK., “Fingerprint Spoof Detection Using Contrast Enhancement and Convolutional Neural Networks”. In Information Science and Applications; Kim, K., Joukov, N., Eds. Springer: Singapore, 2017, pp. 331-338.

Sudiro, Sunny Arief, Disertasi “Fingerprint Recognition using FPGA Devices”, Doctoral Program Gunadarma University, 2009.

V. Mura, L. Ghiani, G. L. Marcialis, F. Roli, D. A. Yambay and S. A. Schuckers, “LivDet fingerprint liveness detection competition 2015”, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2015, pp.1-6, doi:10.1109/BTAS.2015.7358776.

Lin Hong, Yifei Wan and Jain A.K., "Fingerprint image enhancement: algorithm and performance evaluation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, 1998, pp. 777-789, doi: 10.1109/34.709565.

Sagayam, Martin & Ponraj, D. & Winston, J. & Yaspy, J.C. & Jeba, D. & Clara, A. “Authentication of biometric system using fingerprint recognition with euclidean distance and neural network classifier”. International Journal of Innovative Technology and Exploring Engineering. 8. 766-771, 2019

Zhang T., Suen C., “A Fast Parallel Algorithm For Thinning Digital Patterns”. CACM, 27:236239, 1984

Sudiro, S.A.,.”Thinning Algorithm for Image Converted in Fingerprint Recognition System”, National Seminar Soft-Computing Inteligent Systems and Information Technology 2005, Universitas Kristen Petra, Surabaya, 2005

Kasaei S., M. Deriche, and B. Baashash, “Fingerprint Feature Extraction Using Block Direction on Reconstructured Images”. IEEE Tencon (Spech and Image Technologies for computing and Telecommunications), 1997, pages 303-306

Li, Stan Z. and Jain, A.K., “Encyclopedia Of Biometrics”, Springer Science & Business Media, 2009

T. Guo, J. Dong, H. Li and Y. Gao, “Simple convolutional neural network on image classification”, IEEE 2nd International Conference on Big Data Analysis (ICBDA), 2017, pp. 721-724, doi: 10.1109/ICBDA.2017.8078730.




DOI: http://dx.doi.org/10.22441/incomtech.v11i3.13770

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Publisher Address:
Magister Teknik Elektro, Universitas Mercu Buana
Jl. Meruya Selatan 1, Jakarta 11650
Phone (021) 31935454/ 31934474
Fax (021) 31934474
Email: incomtech@mercubuana.ac.id
Website of Master Program in Electrical Engineering
http://mte.pasca.mercubuana.ac.id

pISSN: 2085-4811
eISSN: 2579-6089
Jurnal URL: http://publikasi.mercubuana.ac.id/index.php/Incomtech
Jurnal DOI: 10.22441/incomtech

Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional

.

Web
Analytics Made Easy - StatCounter
View My Stats

The Journal is Indexed and Journal List Title by: