A new algorithm for fingerprint feature extraction without the necessity to improve its image

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

Abstrakty

EN

Based on Gabor filter, an algorithm is worked out and presented in this work. The algorithm uses ridge endings and ridge bifurcation to represent a fingerprint image. The experimental results have proven the algorithm completion in preparing the fingerprint image for simple classification and hence high success rate of recognition. Spurious features from detected set of minutiae are deleted by a postprocessing stage. The detected features are observed to be reliable and accurate. The algorithm was implemented in Matlab and therefore it is under steady modification and improvement as each step can be easily visualized graphically to check for further analysis. The best feature of the algorithm is the unnecessity for noise removal, brightness or contrast improvement, normalization or even histogram equalization.

Słowa kluczowe

EN

biometrics; feature extraction; fingerprints; minutiae; Gabor filtering; feature vector

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2010
• Tom: Vol. 6, no. 12
• Strony: 25--29
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Rapta P.

• Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, Poland

autor

Saeed K.

• Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, Poland

Bibliografia

• 1. Bućko, Ł. (2009). Identyfikator linii papilarnych. Praca magisterska. Politechnika Białostocka (in Polish).
• 2. Greenberg, S., Aladjem, M., Kogan, D., & Dimitrov, I. (2002). Fingerprint Image Enhancement using Filtering Techniques. Real-Time Imaging, Volume 8 , Issue 3, 227-236.
• 3. Hong, L., Wan, Y., & Jain, A. (1998). Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 20, no. 8, 777-789.
• 4. Kim, S., Lee, D., & Kim, J. (2001). Algorithm for Detection and Elimination of False Minutiae in Fingerprint Images. In Lecture Notes in Computer Science (pp. 235-240). Berlin: Springer.
• 5. Maltoni, D., Jain, A. K., Maio, D., & Prabhakar, S. (2003). Handbook of finterprint recognition. New York: Springer.
• 6. Ohtsuka, T., Watanabe, D., & Aoki, H. (2007). Fingerprint Core and Delta Detection by Candidate Analysis. MVA2007 IAPR Conference on Machine Vision Applications, (pp. 130-133). Tokyo, Japan.
• 7. Porwik P., Wiecław Ł. (2008). Local binarization and fingerprint area extraction in recognition systems. BIOMETRYKA. Institute of Mathematical Machines, Warsaw 2008, pp. 97-106.
• 8. Porwik P., Wiecław Ł. (2009). A new fingerprint ridges frequency determination method". IEICE International Journal Electronics Express. Japan. 2009. Vol.6 No.3, pp. 154-160.
• 9. Ratha, N., & Bolle, R. (2004). Automatic Fingerprint Recognition Systems. New York, USA: Springer.
• 10. Saeed K., Tabędzki M., Rybnik M., Adamski M. (2010). K3M – A Universal Algorithm for Image Skeletonization and a Review of Thinning Techniques. International Journal of Applied Mathematics and Computer Science, Vol. 10, No. 2, 2010, pp. 317-335
• 11. Thai, R. (2003). Fingerprint Image Enhancement and Minutiae Extraction. The University of Western Australia.