Comparative Analysis of Fingerprint Features Extraction Methods
Human fingerprint features extraction is important for building a fingerprint classification system. The extracted features must be unique and stable by remaining unchanged for the same fingerprint with various rotation degrees. This paper will analyze the most famous methods used to extract fingerprint features. K-means, LBP, WPT, and minutiae methods will be investigated; the obtained experimental results will be analyzed and compared to raise suitable recommendations for using these methods. The process of extracting the fingerprint's characteristics is one of the important things for use in discrimination systems, which requires saving memory and accelerating the classification process to take the appropriate decision and as quickly as possible. The stability of the properties of the fingerprint image, regardless of the different rotations, will lead to the use of a single features vector for the fingerprint with its different conditions and thus treat all the rotated fingerprints as one fingerprint image. This factor will be studied in this research to recommend the experts interested in building fingerprint recognition systems.
Keywords: fingerprint, features, local binary pattern, K-means, wavelet packet tree, bifurcation, ridge, Euclidean distance.
ALI, M.H., MAHALE, V.H., YANNAWAR, P., and GAIKWAD, A.T. Overview of Fingerprint Recognition System. International Conference on Electrical, Electronics, and Optimization Techniques, 2016: 1334–1338. https://doi.org/10.1109/ICEEOT.2016.7754900
PERICHAPPAN, K.A.P., and SASUBILLI, S. Accurate Fingerprint Enhancement and Identification Using Minutiae Extraction. Journal of Computer and Communications, 2017, 5: 28-38. https://doi.org/10.4236/jcc.2017.514003.
ZAHRAN, B., AYYOUB, B., and NADER, J. Suggested Method to Create Color Image Features Victor. Journal of Engineering and Applied Sciences, 2019, 14(1): 2203-2207.
AL ZUDOOL, M., KHAWATREH, S., and ALQADI, Z. Efficient Methods used to Extract Color Image Features. International Journal of Computer Science and Mobile Computing, 2017, 6(12):7-14.
ABU-FARAJ, M., and ZUBI, M. Analysis and Implementation of Kidney Stones Detection by Applying Segmentation Techniques on Computerized Tomography Scans. Italian Journal of Pure and Applied Mathematics, 2020, 43: 590-602.
GHATASHEH, N., AL-TAHARWA, I., and AL-AHMAD, B. Dead Sea Starvation: Towards Enhanced Monitoring of Water Resources by Modeling Meteorological Variables and Remote Sensing. Journal of Software Engineering and Applications, 2016, 9(12): 588-600. https://doi.org/10.4236/jsea.2016.912040.
SUDIRO, A., PAINDAVOINE, M., and KUSUMA, M. Simple Fingerprint Minutiae Extraction Algorithm Using Crossing Number on Valley Structure. IEEE Workshop in Automatic Identification Advanced Technologies, 2007: 41-44.
KAUR, R., SANDHU, P.S., and KAMRA, A. A novel method for fingerprint feature extraction. International Conference on Networking and Information Technology, 2010: 1-5. https://doi.org/10.1109/ICNIT.2010.5508569.
ALI, M.H., MAHALE, V.H., YANNAWAR, P., and GAIKWAD, A.T. Overview of Fingerprint Recognition System. International Conference on Electrical, Electronics, and Optimization Techniques, 2016: 1334–1338. https://doi.org/10.1109/ICEEOT.2016.7754900.
ROSS, A., JAIN, A., and REISMAN, J. A Hybrid Fingerprint Matcher. Proceedings of International Conference on Pattern Recognition, 2003: 1661–1673. https://doi.org/10.1016/s0031-3203(02)00349-7.
BAZEN, A.M. Fingerprint Identification – Feature Extraction, Matching, and Database Search. University of Twente, Enschede, the Netherlands, 2002: 187.
JAIN, A., CHEN, Y., and DEMIRKUS, M. Pores and Ridges: Fingerprint Matching Using Level 3 Features. 18th International Conference on Pattern Recognition, 2006: 477-480. https://doi.org/10.1109/ICPR.2006.938.
MALTONI, D., MAIO, D., JAIN, A.K., and PRA, S. Handbook of Fingerprint Recognition. USA: Springer Science & Business Media, 2009.
CYNTHIA, D.N., RODRIGUES, L.J., and NAUSHEEDA, B.S. A Survey on Fingerprint Recognition Techniques. International Journal of Latest Trends in Engineering and Technology, 2016: 441-447.
CHEN, Y., and JAIN, A.K. Beyond Minutiae: A Fingerprint Individuality Model with Pattern, Ridge and Pore Features. International Conference on Biometrics, 2009: 523–533. https://doi.org/10.1007/978-3-642-01793-3_54.
COETZEE, L., and BOTHA, E.C. Fingerprint Recognition in Low-Quality Images. Pattern Recognition, 1993, 26(10): 1441–1460. https://doi.org/10.1016/0031-3203(93)90151-l.
DODDS, G.H. Identification by Fingerprints. Australian Journal of Forensic Sciences, 1986, 18(3-4): 136-142. https://doi.org/10.1080/00450618609411204.
GERMAIN, R.S., CALIFANO, A., and COLVILLE, S. Fingerprint Matching Using Transformation Parameter Clustering. IEEE Computational Science and Engineering, 1997, 4(4): 42–49. https://doi.org/10.1109/99.641608.
NEDJAH, N., WYANT, R.S., MOURELLE, L.M., and GUPTA, B.B. Efficient Fingerprint Matching on Smartcards for High Security. Information Sciences, 2019, 479: 622-639. https://doi.org/10.1016/j.ins.2017.12.038.
ZHANG, F.D., XIN, S.Y. and FENG, J.F. Combining Global and Minutia Deep Features for Partial High-Resolution Fingerprint Matching. Pattern Recognition Letters, 2019, 119: 139-147. https://doi.org/10.1016/j.patrec.2017.09.014.
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