Survey of Primary Methods of Fingerprint Feature Extraction
DOI:
https://doi.org/10.14529/ctcr180117Keywords:
fingerprint, methods of recognition, correlation comparison, a comparison of the pattern, equalization, Gabor filterAbstract
Proving the identity of the individual today is an urgent need in many areas, including social security, financial, criminal and other fields. Biometrics is interested in identifying individuals through personality traits, eye color, fingerprints, height, facial appearance, and signature. Image processing technology supports most of the techniques used in biometrics, by improving images and matching images, to identify the individual identity. Fingerprint recognition is some of the most well-known biometrics, and it the best the used biometric result for confirmation on computer systems. Technic fingerprinting has a great popularity for simplicity of acquisition, the use and approval when compared to other systems. Fingerprint Recognition System consists of four steps: firstly, Image acquisition (image fingerprint) through sensors .Secondly, processing the image so that it obscures the noise that exists, clarifying the hills. Thirdly, Extracting features of injury fingerprints. Fourth, compare the acquired features with features of fingerprint fingerprints in databases. The aim of this paper is to present the latest research in the applications of fingerprint recognition systems.
References
Ali M.H., Mahale V.H., Yannawar P., Gaikwad A.T. Overview of Fingerprint Recognition System. International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT, 2016, pp. 1334–1338. DOI: 10.1109/ICEEOT.2016.7754900
Ross A., Jain A., Reisman J. A Hybrid Fingerprint Matcher. Proceedings of International Conference on Pattern Recognition, 2003, pp.1661–1673. DOI: 10.1016/s0031-3203(02)00349-7
Bazen A.M. Fingerprint Identification – Feature Extraction, Matching, and Database Search. Univ. of Twente, Enschede, The Netherlands, 2002. 187 p.
Chen Y., Jain A.K. Beyond Minutiae: A Fingerprint Individuality Model with Pattern, Ridge and Pore Features. International Conference on Biometrics, 2009, pp. 523–533, DOI: 10.1007/978-3-642-01793-3_54
Coetzee L., Botha E.C. Fingerprint Recognition in Low Quality Images. Pattern Recognition, 1993, vol. 26, no. 10, pp. 1441–1460. DOI: 10.1016/0031-3203(93)90151-l
Dodds G.H. Identification by Fingerprints. Aust. J. Forensic Sci, 1986, vol. 18, no. 3–4, p. 136. DOI: 10.1080/00450618609411204
Ferris S., Powers R.L., Lindh T. Hyperladder Fingerprint Matcher, 1997. 36 p.
Germain R.S., Califano A., Colville S. Fingerprint Matching Using Transformation Parameter Clustering. IEEE Computational Science and Engineering, 1997, vol. 4, iss. 4, pp. 42–49. DOI: 10.1109/99.641608
Gonzalez R.C. Woods R.E. Digital Image Processing. Editors. A. Dworkin, J. McDonnell. Tom Robbins Pub., 2002. 812 p.
Gorman L.O. Fingerprint Verification. Springer, Boston, MA, 1998, vol. 3, no. 1, pp 43–64.
Jain A., Chen Y., Demirkus M. Pores and Ridges: Fingerprint Matching Using Level 3 Features. Engineering, 2006.
Maltoni D. Maio D., Jain A.K., Pra. S. Handbook of Fingerprint Recognition. USA, Springer Science & Business Media, 2009. 417 p.
Cynthia D.N., Rodrigues L.J., Nausheeda B.S. A Survey on Fingerprint Recognition Techniques. International Journal of Latest Trends in Engineering and Technology, 2016, pp. 441–447.
Mahadik S., Narayanan K., Bhoir D.V., Shah D., Access Control System Using Fingerprint Recognition. International Conference on Advances in Computing, Communication and Control, 2009, pp. 306–311. DOI: 10.1145/1523103.1523166
Yang J., Shin j. W., Min B., Park J. B., Park D. Fingerprint Matching Using Invariant Moment FingerCode and Learning Vector Quantization Neural Network. IEEE Conference Publications on Computational Intelligence and Security, 2006, Vol. 1, pp. 735– 738, DOI: 10.1109/ICCIAS.2006.294231






