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Tytuł artykułu

Gradient based fingerprint orientation field estimation

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Accurate estimation of ridge orientation is a critical step in image preprocessing methods used in automatic fingerprint identification systems (AFIS). Fingerprint orientation plays important roles in fingerprint enhancement, classification and recognition. The most popular is gradient-based method. This paper reviews the algorithm parameters, determining the compromise between accuracy in high-curvature areas and robustness against noise.
Rocznik
Tom
Strony
203--207
Opis fizyczny
Bibliogr. 16 poz., rys., tab., wykr.
Twórcy
autor
  • University of Silesia, Institute of Computer Science, 41-200 Sosnowiec, Będzińska 39, Poland
Bibliografia
  • [1] 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., 2002, Vol. 24, No. 7, pp. 905-919.
  • [2] CAPPELLI R., MAIO D., WAYMAN J. L., JAIN A. K., Performance evaluation of fingerprint verification systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, Vol. 28, No. 1, pp. 3-18.
  • [3] CANNY J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, Vol. 8, No. 6, pp. 679-698.
  • [4] CHIKKERUR S., CARTWRIGHT A. N., GOVINDARAJU V., Fingerprint enhancement using STFT analysis. Pattern Recogn. 2007, Vol. 40, No. 1, pp. 198-211.
  • [5] COSTA S. M., FERNANDEZ F. J., OLIVEIRA J. M., A New Paradigm on Fingerprint Classification using Directional Image. SIBGRAPI, 405, 2002.
  • [6] HONG L., JAIN A. K., WAN Y., Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1998, Vol. 20, No. 8, pp. 777-789.
  • [7] HALICI U., ONGUN G., Fingerprint classification through self-organizing feature maps modified to treat uncertainties. Proc. of the IEEE, 1996, Vol. 84, No. 10, pp. 1497-1512.
  • [8] HONG L., JAIN A. K., PRABHAKAR S., A Multichannel Approach to Fingerprint Classification. IEEE Trans. Pattern Anal. Mach. Intell. 1999, Vol. 21, No. 4, pp. 348-359.
  • [9] HONG L., JAIN A. K., PANKANTI S., PRABHAKAR S., Filterbank-based fingerprint matching. IEEE Trans. Image Processing, 2000, Vol. 9, No. 5, pp. 846-859.
  • [10] JAIN A. K., KARU K., Fingerprint classification. Pattern Recognition, 1996, Vol. 29, No. 3, pp. 38-44.
  • [11] KASS M., WITKIN A., Analyzing Orientated Pattern. Computer Vision, Graphics and Image Processing, 1987, Vol. 37, pp. 362-397.
  • [12] LIU M., JIANG X., KOT A. C., Fingerprint reference-point detection. EURASIP J. Appl. Signal Process. 2005, pp. 498-509.
  • [13] WIECLAW L., Fingerprint Orientation Field Enhancement. Computer Recognition Systems 4, Advances in Intelligent and Soft Computing, Springer-Verlag, 2011, Vol. 95, pp. 33-40.
  • [14] WROBEL 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, pp. 59-64, 2009.
  • [15] Biometrics Market and Industry Report 2009-2014. International Biometric Group, New York, 2009.
  • [16] FVC-onGoing: on-line evaluation of fingerprint recognition algorithms. Fingerprint Orientation Extraction Benchmark: https://biolab.csr.unibo.it/fvcongoing/UI/Form/BenchmarkAreas/BenchmarkAreaFOE.aspx.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bf861c74-15be-4e11-b85d-ad79bdf5769b
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