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The pixel alignment based algorithm for continuous orientation field estimation

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a new estimation method of fingerprint orientation field. An accurate estimation of fingerprint orientation fields is an essential step in automatic fingerprint recognition systems (AFIS). Most popular, gradient-based method is very sensitive to noise (image quality). Proposed algorithm is a modification of, more resistant to noise, mask-based method, which provides orientation limited to discrete values. This modification is based on aggregation of pixel values differentiation and was used to more precise estimation of magnitude of orientation vectors. This approach allows to obtain a continuous values of orientation field still maintaining robust to noise.
Rocznik
Tom
Strony
47--52
Opis fizyczny
Bibliogr. 19 poz., rys.
Twórcy
  • University of Silesia, Institute of Computer Science, 41-200 Sosnowiec, Będzińska 39, Poland
autor
Bibliografia
  • [1] JAIN A.K., BOLLE R.,PANKANTI S., Biometrics: Personal Identification in Networked Society. Kluwer, New York, 1999.
  • [2] HONG L., JAIN A. K., WAN Y., Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, 1998, pp. 777–789.
  • [3] CHIKKERUR S., CARTWRIGHT A. N., GOVINDARAJU V., Fingerprint enhancement using STFT analysis. Pattern Recogn. Vol. 40, No. 1, 2007, pp.198–211.
  • [4] LIU M., JIANG X., KOT A. C.: Fingerprint reference–point detection. EURASIP J. Appl. Signal Process. 2005, pp.498–509.
  • [5] BAZEN A. M. GEREZ S. H., Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. Vol. 24, No. 7, 2002, pp. 905–919.
  • [6] WRÓBEL K. DOROZ R., New Method For Finding a Reference Point in Fingerprint Images With the Use Of The IPAN99 Algorithm. Journal of Medical Informatics & Technologies. Vol. 13, 2009, pp.59–64.
  • [7] HONG L., JAIN A. K., PRABHAKAR S., A Multi–channel Approach to Fingerprint Classification. IEEE Trans. Pattern Anal. Mach. Intell. Vol. 21, No. 4, 1999, pp.348–359.
  • [8] COSTA S. M., FERNANDEZ F. J., OLIVEIRA J. M., A New Paradigm on Fingerprint Classification using Directional Image. SIBGRAPI, 2002, pp. 405.
  • [9] JAIN A. K., KARU K., Fingerprint classification. Pattern Recognition, Vol. 29, No. 3, pp. 38–44, 1996.
  • [10] HALICI U., ONGUN G., Fingerprint classification through self–organizing feature maps modified to treat uncertainties. Proc. of the IEEE, Vol. 84, No. 10, 1996, pp.1497–1512.
  • [11] HONG L., JAIN A. K., PANKANTI S., PRABHAKAR S., Filterbank–based fingerprint matching. IEEE Trans. Image Processing, Vol. 9, No. 5, 2000, pp.846–859.
  • [12] MAIO D., MALTONI D., Direct gray–scale minutiae detection in fingerprints. IEEE Trans. Pattern Anal. Mach. Intell.Vol. 19, No.1, 1997, pp.27–45.
  • [13] KASS M., WITKIN A., Analyzing Orientated Pattern. Computer Vision, Graphics and Image Processing, Vol. 37, 1987, pp.362–397.
  • [14] HONG L., JAIN A. K., On–line Fingerprint Verification. IEEE Trans. Pattern Anal. and Mach. Intell. Vol.19, No.4, 1997, pp.302–314.
  • [15] PORWIK P., WIĘCŁAW Ł., A new approach to reference point location in fingerprint recognition. IEICE Int. Journal Electronics Express. Japan. Vol. 1, No. 18, 2004, pp. 575–581.
  • [16] PORWIK P., WIĘCŁAW Ł., The New Efficient Method of Fingerprint Image Enhancement. Int. Journal of Biometrics. Inderscience Publisher, Vol. 1, 2008, No.1, pp.36–46.
  • [17] http://fingerprint.nist.gov
  • [18] ZHOU J., GU J., A model–based method for the computation of fingerprints' orientation field. IEEE Transactions on Image Processing, Vol. 13, No. 6, 2004, pp. 821–835.
  • [19] DASS S. C., Markov random field models for directional field and singularity extraction in fingerprint images. IEEE Transactions on Image Processing, Vol. 13, No. 10, 2004, pp. 1358–1367.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0017-0010
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