Geodesic
Verification of the algorithm for estimating the flow chart of fingerprint images
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 7 (2018) no. 4, pp. 67-82 Cet article a éte moissonné depuis la source Math-Net.Ru

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The construction of the flow chart for fingerprints digital images is an important step in biometric identification and comparison procedure. In particular, the results of building the flow chart significantly affect the filtering of such attributes of images as minutiae, loops, deltas and curls. In addition, improving the quality of the flow chart construction can significantly reduce the overall identification error. This article describes the developed algorithm for constructing the flow chart of fingerprints digital images. The proposed algorithm is based on an improved method of gradients. This method is applied in two stages with different sizes of aperture and two types of smoothing: common and along the direction of the pattern lines. The quality of this algorithm was evaluated using a web-framework created on the platform of the University of Bologna (Italy). This framework is created for automatic, remote evaluation of the results of different fingerprint recognition algorithms including the estimation of the flow chart. The requirements for the program structure, input and output data imposed by this framework were fulfilled. The outputs of the algorithm work evaluation by the selected framework are considered and analyzed. A comparison of the algorithm results evaluation with the original gradient method is made, as well as with the results of other test participants published in open access.
Keywords: biometrical identification, fingerprint flow chart, pattern recognition, fingerprints, verification. 
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A. V. Agafonov; D. S. Rozhina. Verification of the algorithm for estimating the flow chart of fingerprint images. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 7 (2018) no. 4, pp. 67-82. http://geodesic.mathdoc.fr/item/VYURV_2018_7_4_a4/

[1] V. I. Zadorozhnyj, “Finrerprint Identification”, PC Magazine, 2004, no. 2, 114–119

[2] A. V. Gasparyan, A. A. Kirakosyan, “A System for Comparing Fingerprints Based on Local Characteristics”, Bulletin of RAU. Series: Physics and Mathematics Sciences, 3 (2006), 85–91

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

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

[5] 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

[6] G. Carsten, T. Benjamin, H. Stephan, “Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models”, IET Biometrics, 6:3 (2017), 183–190 | DOI

[7] L. Wieclaw, “Fingerprint Orientation Field Enhancement”, Computer Recognition Systems 4, 95 (2011), 33–40 | DOI

[8] E. M. Orlov, Method for Verification and Identification Fingerprints } {\tt http://www.findpatent.ru/patent/231/2310910.html

[9] V. Ju. Gudkov, Method for Encoding Fingerprints } {\tt http://www.findpatent.ru/patent/241/2413300.html

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

[13] NIST. Biometric Evaluations Homepage } {\tt https://www.nist.gov/itl/iad/imagegroup/resources/biometrics-evaluations

[14] Biometrics Ideal Test. The First CCBR Competition on Fingerprint Recognition } {\tt http://biometrics.idealtest.org/2014/CCFP2014.jsp

[15] D. Maltoni, D. Maio, A. K. Jain, Handbook of Fingerprint Recognition, SpringerVerlag, New York, 2003, 348 pp. | DOI

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

[17] D. Rodgers, Procedural Elements for Computer Graphics, WCB/McGraw-Hill Inc, 1989, 54–63 pp.