Geodesic
Robust fingerprint flow chart algorithm
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 8 (2019) no. 4, pp. 43-55 Cet article a éte moissonné depuis la source Math-Net.Ru

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The orientation field is an important characteristic of human skin patterns and has a significant impact on the results of fingerprint identification. The methods of constructing the orientation field based on the gradient are diverse, but they are united by a high sensitivity to noise and defects that appear on the images during the formation of tracks. The article proposes a new method for constructing a stream field for digital images of fingerprints. The method allows to improve the solution of a number of key tasks of image processing, including the task of predicting the direction of lines in the area of skin folds, scars and other finger surface defects. The method relies on such approaches as image processing at the subpixel level, on cluster analysis of the field of image gradients and consists in the sequential application of several algorithms. These are image interpolation and estimation of gradient values on it, convolution of the gradient field with a given pattern for noise control at the subpixel level, selection of reference areas based on the construction of local quality estimates of the pattern, prediction of the direction field from reference areas over the entire image with the adaptation of the predicted values for the measurement results. The results of the proposed method were verified using the web framework created on the basis of the University of Bologna in Italy. The new verification results are compared with the verification results of the earlier method developed by the authors, and with other published algorithms on the same web framework.
Keywords: biometrical identification, fingerprint orientation field, pattern recognition, fingerprints, verification. 
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     title = {Robust fingerprint flow chart algorithm},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a Vy\v{c}islitelʹna\^a matematika i informatika},
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A. V. Agafonov; D. S. Rozhina; H. I. Wahhab; A. N. Alanssari. Robust fingerprint flow chart algorithm. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 8 (2019) no. 4, pp. 43-55. http://geodesic.mathdoc.fr/item/VYURV_2019_8_4_a3/

[1] B. Weixin, D. Shifei, X. Yu, “An Improved Fingerprint Orientation Field Extraction Method Based on Quality Grading Scheme”, International Journal of Machine Learning and Cybernetics, 9:8 (2018), 1249–1260 | DOI

[2] V. Ju. Gudkov, A. V. Bojcov, “Enhancement of Fingerprint Images with the Gabor Filter”, Bulletin of South Ural State University. Series: Mathematical Modeling, Programming Computer Software, 15:1 (2015), 128–132

[3] M. Saparudin, S. Ghazali, “A Technique to Improve Ridge Flows of Fingerprint Orientation Fields Estimation”, Telkonomika, 14 (2016), 987–998 | DOI

[4] Y. Wang, H. Jiankun, S. Heiko, “A Gradient Based Weighted Averaging Method for Estimation of Fingerprint Orientation Fields”, Digital Image Computing: Techniques and Applications, 2005 | DOI

[5] G. Carsten, T. Benjamin, H. Stephan, “Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models”, IET Biometrics, 2017, 183–190 | DOI

[6] L. Wieclaw, “Fingerprint Orientation Field Enhancement”, Computer Recognition Systems 4, 2011, 33–40 | DOI

[7] M. Khachay, M. Pasynkov, “Theoretical approach to developing efficient algorithms of fingerprint enhancement”, Analysis of Images, Social Networks and Texts. Communications in Computer and Information Science (CCIS), Springer, 2015, 83–95 | DOI

[8] A. V. Agafonov, D. S. Rozhina, “Verification of the Algorithm for Estimating the Flow Chart of Fingerprint Images”, Bulletin of South Ural State University. Series: Computational Mathematics and Computer Science, 7:4 (2018), 67–82 | DOI

[9] Biometric System Laboratory. FVC-onGoing: on-line evaluation of fingerprint recognition algorithms } {https://biolab.csr.unibo.it/FvcOnGoing/UI/Form/Home.aspx

[10] R. Gonsalez, E. Woods, Digital Image Processing, Prentice Hall, 2001, 794 pp.

[11] B. Jane, Digital Image Processing: Concepts, Algorithms and Scientific applications, Springer Verlag, 2005, 589 pp.

[12] D. Maltoni, D. Maio, A. K. Jain, Handbook of Fingerprint Recognition, Springer-Verlag, New York:, 2003, 348 pp.

[13] A. Bazen, Fingerprint Identification: Feature Extraction, Matching, and Database Search, Univ. of Twente, The Netherlands, 2002, 187 pp.