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2021 | OriginalPaper | Chapter

Search of Informative Biometric Characteristic Features of the Palm Based on Parallel Shift Technology

Authors : Sergey Yuzhakov, Sergii Bilan, Stepan Bilan, Mykola Bilan

Published in: Biometric Identification Technologies Based on Modern Data Mining Methods

Publisher: Springer International Publishing

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Abstract

Among all the biometric characteristic features of a person, the geometric shape of his palm is often used. The geometric shape of the palm is different for each person in more than 90 known parameters. However, a person can be confidently distinguished from another person using all the known biometric characteristics of the palm. Moreover, part of the quantitative values of such characteristics may coincide for several people. This is especially actual for relatives. Therefore, in this chapter the task is to find the most informative biometric characteristics of the palm, which determine the significant differences between people. To solve this problem, parallel shear technology is used. The palm image is divided into areas that make up the area of the rectangle, divided into six areas (three areas on the right and three areas on the left), two internal horizontal lines, and one vertical line. Obtained images of regions are processed using parallel shift technology, and their function of areas of intersection for different directions of shift of the image copy is determined. Relations of average values for each direction of the shift of the copy and for the images of each area are determined. A database of quantitative relations for each image is formed. The percentage of coincidence of input and reference data is analyzed and a decision is made on the identification of a person. Two quantitative characteristics were used, which determined experimentally the accuracy of identification and the most informative images of palm sites. As a result of the experiment, the most informative areas of the palm were determined, which allowed to obtain high results of biometric identification.

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Literature
1.
go back to reference Nambiar, A., Bernardino, A., & Nascimento, J. C. (2019). Gait-based person re-identification: A survey. ACM Computing Surveys, 52(2), 33.CrossRef Nambiar, A., Bernardino, A., & Nascimento, J. C. (2019). Gait-based person re-identification: A survey. ACM Computing Surveys, 52(2), 33.CrossRef
2.
go back to reference Fairhurst, M. (2019). Biometrics: A very short introduction (very short introductions). Oxford: Oxford University Press. Fairhurst, M. (2019). Biometrics: A very short introduction (very short introductions). Oxford: Oxford University Press.
3.
go back to reference Das, R. (2018). The science of biometrics: Security Technology for Identity Verification. Abingdon: Routledge.CrossRef Das, R. (2018). The science of biometrics: Security Technology for Identity Verification. Abingdon: Routledge.CrossRef
4.
go back to reference Li, S. Z., & Jain, A. K. (2015). Encyclopedia of biometrics. Berlin: Springer.CrossRef Li, S. Z., & Jain, A. K. (2015). Encyclopedia of biometrics. Berlin: Springer.CrossRef
5.
go back to reference Hennings-Yeomans, P. H., Kumar, B. V. K., & Savvides, M. (2007). Palmprint classification using multiple advanced correlation filters and palm-specific segmentation. IEEE Trans. Info Forensics & Security, 2(3), 613–622.CrossRef Hennings-Yeomans, P. H., Kumar, B. V. K., & Savvides, M. (2007). Palmprint classification using multiple advanced correlation filters and palm-specific segmentation. IEEE Trans. Info Forensics & Security, 2(3), 613–622.CrossRef
6.
go back to reference Kumar, A. (2008). Incorporating cohort information for reliable Palmprint authentication. In Sixth Indian Conference on Computer Vision, Graphics & Image Processing (pp. 583–590). Kumar, A. (2008). Incorporating cohort information for reliable Palmprint authentication. In Sixth Indian Conference on Computer Vision, Graphics & Image Processing (pp. 583–590).
7.
go back to reference Genovese, A., Piuri, V., & Scotti, F. (2014). Touchless Palmprint recognition systems. Berlin: Springer International Publishing.CrossRef Genovese, A., Piuri, V., & Scotti, F. (2014). Touchless Palmprint recognition systems. Berlin: Springer International Publishing.CrossRef
8.
go back to reference Qiu, Z., et al. (2019). Local Discriminative Direction Extraction for Palmprint Recognition. In Biometric Recognition. 14th Chinese Conference, CCBR 2019, Zhuzhou, China, October 12–13, 2019, Proceedings (pp. 3–11).CrossRef Qiu, Z., et al. (2019). Local Discriminative Direction Extraction for Palmprint Recognition. In Biometric Recognition. 14th Chinese Conference, CCBR 2019, Zhuzhou, China, October 12–13, 2019, Proceedings (pp. 3–11).CrossRef
9.
go back to reference Zhang, D., Kong, W. K., You, J., & Wong, M. (2003). On-line palmprint identification. IEEE Trans. Patt. Anal. Machine Intell., 25, 1041–1050.CrossRef Zhang, D., Kong, W. K., You, J., & Wong, M. (2003). On-line palmprint identification. IEEE Trans. Patt. Anal. Machine Intell., 25, 1041–1050.CrossRef
10.
go back to reference Sneha, M., & Dhananjay.M. (2013). Palmprint authentication using SIFT. International Journal of Engineering Research & Technology (IJERT)., 2(9), 2439–2444. Sneha, M., & Dhananjay.M. (2013). Palmprint authentication using SIFT. International Journal of Engineering Research & Technology (IJERT)., 2(9), 2439–2444.
11.
go back to reference Bilal, A., Youssef, C., & Amina, S. (2018). Geometrical local image descriptors for palmprint recognition. In International Conference on Image and Signal Processing 2018 (ICISP 2018), Jul 2018, Cherbourg, France (pp. 419–426). Bilal, A., Youssef, C., & Amina, S. (2018). Geometrical local image descriptors for palmprint recognition. In International Conference on Image and Signal Processing 2018 (ICISP 2018), Jul 2018, Cherbourg, France (pp. 419–426).
12.
go back to reference Fei, L., Xu, Y., Tang, W., & Zhang, D. (2016). Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recognition., 49, 89–101.CrossRef Fei, L., Xu, Y., Tang, W., & Zhang, D. (2016). Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recognition., 49, 89–101.CrossRef
13.
go back to reference Inass S. H., ShamsuL B. S., Md Jan N., & Nilam N. B. A. S. (2019). Multimodal palmprint technology: A review. Journal of Theoretical and Applied Information Technology. 97(11): 2882–2896. Inass S. H., ShamsuL B. S., Md Jan N., & Nilam N. B. A. S. (2019). Multimodal palmprint technology: A review. Journal of Theoretical and Applied Information Technology. 97(11): 2882–2896.
14.
go back to reference Bilan, S., & Yuzhakov, S. (2018). Image processing and pattern recognition based on parallel shift technology. Taylor & Francis Group: CRC Press. Bilan, S., & Yuzhakov, S. (2018). Image processing and pattern recognition based on parallel shift technology. Taylor & Francis Group: CRC Press.
15.
go back to reference Bilan, S., Al-zoubi, S. I., Yuzhakov, S., & Bilan, M. (2018). Description and recognition of symmetrical and freely oriented images based on parallel shift technology. In 5th International Conference on Mathematics and Computers in Sciences and Industry (MCSI) (pp. 86–91). Athens: IEEE.CrossRef Bilan, S., Al-zoubi, S. I., Yuzhakov, S., & Bilan, M. (2018). Description and recognition of symmetrical and freely oriented images based on parallel shift technology. In 5th International Conference on Mathematics and Computers in Sciences and Industry (MCSI) (pp. 86–91). Athens: IEEE.CrossRef
16.
go back to reference Bilan, S., Yuzhakov, S., & Bilan, S. (2014). Saving of etalons in image processing systems based on the parallel shift technology. Advances in Image and Video Processing, 2(6), 36–41.CrossRef Bilan, S., Yuzhakov, S., & Bilan, S. (2014). Saving of etalons in image processing systems based on the parallel shift technology. Advances in Image and Video Processing, 2(6), 36–41.CrossRef
17.
go back to reference Belan, S., & Yuzhakov, S. (2013). A homogenous parameter set for image recognition based on area. Computer and Information Science, 6(2), 93–102.CrossRef Belan, S., & Yuzhakov, S. (2013). A homogenous parameter set for image recognition based on area. Computer and Information Science, 6(2), 93–102.CrossRef
18.
go back to reference Belan, S., & Yuzhakov, S. (2013). Machine vision system based on the parallel shift technology and multiple image analysis. Computer and Information Science, 6(4), 115–124.CrossRef Belan, S., & Yuzhakov, S. (2013). Machine vision system based on the parallel shift technology and multiple image analysis. Computer and Information Science, 6(4), 115–124.CrossRef
19.
go back to reference Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.MATH Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.MATH
20.
go back to reference Bilan, S. (2017). Formation methods, models, and hardware implementation of pseudorandom number generators: Emerging research and opportunities. Philadelphia: IGI Global. Bilan, S. (2017). Formation methods, models, and hardware implementation of pseudorandom number generators: Emerging research and opportunities. Philadelphia: IGI Global.
21.
go back to reference Bilan, S. M., & Al-Zoubi, S. I. (2019). Handbook of research on intelligent data processing and information security systems (p. 470). Philadelphia: IGI Global. Bilan, S. M., & Al-Zoubi, S. I. (2019). Handbook of research on intelligent data processing and information security systems (p. 470). Philadelphia: IGI Global.
22.
go back to reference Bilan, S., Motornyuk, R., & Bilan, S. (2014). Method of hardware selection of characteristic features based on radon transformation and not sensitive to rotation, shifting and scale of the input images. Advances in Image and Video Processing, 2(4), 12–23.CrossRef Bilan, S., Motornyuk, R., & Bilan, S. (2014). Method of hardware selection of characteristic features based on radon transformation and not sensitive to rotation, shifting and scale of the input images. Advances in Image and Video Processing, 2(4), 12–23.CrossRef
23.
go back to reference Belan, S. N., & Motornyuk, R. L. (2013). Extraction of characteristic features of images with the help of the radon transform and its hardware implementation in terms of cellular automata. Cybernetics and Systems Analysis., 49(1), 7–14.CrossRef Belan, S. N., & Motornyuk, R. L. (2013). Extraction of characteristic features of images with the help of the radon transform and its hardware implementation in terms of cellular automata. Cybernetics and Systems Analysis., 49(1), 7–14.CrossRef
24.
go back to reference Motornyuk, R.L. (2013). Computer-aided methods for identifying images of moving objects based on cellular automata with a hexagonal coating. Dissertation for the degree of candidate of technical sciences (UDC 004.932: 519.713 (043.3)), Kiev: SUIT. Motornyuk, R.L. (2013). Computer-aided methods for identifying images of moving objects based on cellular automata with a hexagonal coating. Dissertation for the degree of candidate of technical sciences (UDC 004.932: 519.713 (043.3)), Kiev: SUIT.
Metadata
Title
Search of Informative Biometric Characteristic Features of the Palm Based on Parallel Shift Technology
Authors
Sergey Yuzhakov
Sergii Bilan
Stepan Bilan
Mykola Bilan
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-48378-4_10