An improved algorithm for feature extraction from a fingerprint fuzzy image

• Autorzy: Surmacz K. , Saeed K. , Rapta P.
• Języki publikacji: EN

Abstrakty

EN

Proper fingerprint feature extraction is crucial in fingerprint-matching algorithms. For good results, different pieces of information about a fingerprint image, such as ridge orientation and frequency, must be considered. It is often necessary to improve the quality of a fingerprint image in order for the feature extraction process to work correctly. In this paper we present a complete (fully implemented) improved algorithm for fingerprint feature extraction, based on numerous papers on this topic. The paper describes a fingerprint recognition system consisting of image preprocessing, filtration, feature extraction and matching for recognition. The image preprocessing includes normalization based on mean value and variation. The orientation field is extracted and Gabor filter is used to prepare the fingerprint image for further processing. For singular point detection, the Poincaré index with a partitioning method is used. The ridgeline thinning is presented and so is the minutia extraction by CN algorithm. The paper contains the comparison of obtained results to the other algorithms.

Słowa kluczowe

EN

biometrics; feature extraction; fingerprints; minutiae; Gabor filter; feature vector; image processing

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2013
• Tom: Vol. 43, nr 3
• Strony: 515--527
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Surmacz K.

• AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, al. Mickiewicza 30, PL-30059 Cracow, Poland

autor

Saeed K.

autor

Rapta P.

Bibliografia

• [1] SAEED K., NAGASHIMA T., Biometrics and Kansei Engineering, Springer, NY, 2012.
• [2] BHANU B., BOSHRA M., XUEJUN TAN, Logical templates for feature extraction in fingerprint images, [In] 15th International Conference on Pattern Recognition, Proceedings, Vol. 2, 2000, pp. 846–850.
• [3] CHEN Y., JAIN A.K., Beyond minutiae: a fingerprint individuality model with pattern, ridge and pore features, International Conference on Biometrics, 2009.
• [4] ESPINOSA-DURO V., Minutiae detection algorithm for fingerprint recognition, IEEE Aerospace and Electronic Systems Magazine 17(3), 2002, pp. 7–10.
• [5] HONG L., WAN Y., JAIN A., Fingerprint image enhancement: algorithm and performance evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 1998, pp. 777–789.
• [6] PRABHAKAR S., Fingerprint Classification and Matching Using a Filterbank, Ph.D. Thesis, Michigan State University, 2001.
• [7] RATHA N.K., SHAOYUN CHEN, JAIN A.K., Adaptive flow orientation-based feature extraction in fingerprint images, Pattern Recognition 28(11), 1995, pp. 1657–1672.
• [8] THAI R., Fingerprint Image Enhancement and Minutiae Extraction, The University of Western Australia, 2003.
• [9] PORWIK P., WIECLAW L., A new fingerprint ridges frequency determination method, IEICE International Journal Electronics Express 6(3), 2009, pp. 154–160.
• [10] ARCELLI C., DI BAJA G.S., A width independent fast thinning algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7(4), 1985, pp. 463–474.
• [11] OHTSUKA T., WATANABE D., AOKI H., Fingerprint core and delta detection by candidate analysis, MVA2007 IAPR Conference on Machine Vision Applications, Tokyo, Japan, 2007, pp. 130–133.
• [12] BUCKO L., Fingerprint Identification, M.Sc. Thesis, Białystok University of Technology, Poland, 2009, (in Polish: Identyfikator linii papilarnych – Praca magisterska).