Abstract
Due to increased requirements for access control systems, the use of biometric recognition technologies is becoming a reliable solution for the protection of critical information. One of the best ways of personal identification is to use the palm vein structure. The chapter deals with improving accuracy in the problem of recognizing the palm vein pattern when comparing biometric templates using the Canny edge detection algorithm and the Gabor filter. 2D Gabor filter improves the adaptability of recognition and is therefore proposed to solve the problem of image blurring and select a threshold when the traditional Canny algorithm smoothes the edges. The results of experiments show that this filter can detect less pronounced edges and provides more complete information about the image, which has a positive effect on the result of biometric authentication. The similarity of two biometric templates is determined using the Minkowski metric. Experiments conducted on the original facility show high performance, as well as good results in false acceptance errors (FAR = 0%) and false rejection errors (FRR = 0.01%) based on processing 360 images captured from 26 people, which makes it possible to use the proposed method in the identification and authentication system at existing data security facilities.
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References
Wu, W., Elliott, S. J., Lin, S., Sun, S., Tang, Y.: Review of palm vein recognition. IET Biom. 9(1), 1–10 (2019)
Miura, N.: Feature extraction of finger-vein patterns based on repeated line tracking and its applications to personal identification. In: Miura, N., Nagasaka, A., Miyatake, T. (eds.) Machine Vision and Applications, pp. 194–203 (2004)
Antipov, R.S., Martynenko, T.V.: Automated access control and management system based on the analysis of human biometric parameters. Comput. Sci. Cybern. 1(15), 21–26 (2019). DonNTU Publ., Donetsk
Grizhebovskaya, A.G., Mikhalev, A.V.: A biometric method of identification of a person by the vascular pattern of the finger. Cybersecurity 5(33), 51–56 (2019)
Sakharova, M.A.: Fingerprint image processing using the Gabor filter. Act. Probl. Aviat. Astronaut 2, 167–169 (2018)
Canny, F.J.A.: computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)
Kim, Y.W., Oh, A.R., Krishna, A.V.: Analyzing the performance of canny edge detection on interpolated Images. In: International Conference on Information and Communication Technology Convergence (ICTC). – June, 2018. https://www.cnki.net/kcms/doi/10.14132/j.cnki.1673-5439.2018.03.011.htm. Accessed 12 Feb 2020
Fu, F., Wang, C., Li, Y.,Fan, H.: An improved adaptive edge detection algorithm based on canny. In: Sixth International Conference on Optical and Photonic Engineering. – July, 2018. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10827/2500361/An-improved-adaptive-edge-detection-algorithm-based-on-Canny/10.1117/12.2500361.short. Accessed 14 Feb 2020
Lepsky, A.E., Bronevich, A.G.: Mathematical Methods For Pattern Recognition: Course of Lectures. Taganrog: TTI SFU Publ., 155 p (2009)
Suyatinov, S.: Bernstein’s theory of levels and its application for assessing the human operator state. In:Dolinina, O., et al. (eds.) Springer Nature Switzerland AG, pp. 298–312 (2019). ICIT 2019, SSDC 199. https://doi.org/10.1007/978-3-030-12072-6_25
Matokhina, A.: Method of the exoskeleton assembly synthesis on the base of anthropometric characteristics analysis. Stud. Syst. Decis. Control 259, 361–393
Agafonov, V.: Super-resolution approach to increasing the resolution of image. In: Kravets A., Shcherbakov M., Kultsova M., Iijima T. (eds.) Knowledge-Based Software Engineering. JCKBSE 2014. Communications in Computer and Information Science, vol. 466. Springer, Cham (2014)
Xin, M., Xiaojun, J.: Palm vein recognition method based on fusion of local Gabor histograms. J. China Univ. Posts Telecommun. 24(6), 55–66 (2017). https://doi.org/10.1016/s1005-8885(17)60242-5
Liu, J., Jing, X.J., Sun, S.L., et al.: Local Gabor dominant direction pattern for face recognition. Chin. J. Electron. 24(2), 245–250 (2015)
Wang, J.G., Yau, W.Y., Suwandy, A., et al.: Fusion of palmprint and palm vein images for person recognition based on “Laplacianpalm” feature. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07), Jun 17 − 22, 2007, 8 p. IEEE, Minneapolis, MN, USA. Piscataway, NJ, USA (2007)
Wang, L.Y., Leedham, G., Cho, D.S.Y.: Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern Recogn. 41(3), 920–929 (2008)
Kulkarni, S., Raut, R.D., Dakhole, P.K.: A Novel authentication system based on hidden biometric trait. Procedia Comput. Sci. 85, 255–262 (2016). https://doi.org/10.1016/j.procs.2016.05.229
Chunyi, L., Mingzhong, L., Xiao, S.: A finger vein recognition algorithm based on gradient correlation. AASRI Procedia 1, 40–45 (2012). https://doi.org/10.1016/j.aasri.2012.06.008
Qiu, S., Liu, Y., Zhou, Y., Huang, J., Nie, Y.: Finger-vein recognition based on dual-sliding window localization and pseudo-elliptical transformer. Expert Syst. Appl. 64, 618–632 (2016). https://doi.org/10.1016/j.eswa.2016.08.031
Joseph, R.B., Ezhilmaran, D.: A smart computing algorithm for finger vein matching with affine invariant features using fuzzy image retrieval. Procedia Comput. Sci. 125, 172–178 (2018). https://doi.org/10.1016/j.procs.2017.12.024
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Safiullina, L.K., Maturov, R.R. (2021). Image Processing for Biometric Scanning of the Palm Vein Pattern. In: Kravets, A.G., Bolshakov, A.A., Shcherbakov, M. (eds) Society 5.0: Cyberspace for Advanced Human-Centered Society. Studies in Systems, Decision and Control, vol 333. Springer, Cham. https://doi.org/10.1007/978-3-030-63563-3_3
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