RECONSTRUCTION CREASE FOR FINGERPRINT IMAGES USING MINUTIA DENSITY DISTRIBUTION: AN EMPIRICAL STUDY

Authors

  • Firas S. Abdulameer Department of physics, College of Science, Mustansiriyah University, Baghdad, Iraq
  • Firas S. Abdulameer
  • Kuba Hasanien Kariem

Keywords:

Fingerprint recognition, Creases recognition, Wrinkles repair, Minutia density

Abstract

Fingerprint recognition is one of the secure techniques for automatic personal identification, and it is one of the safe techniques for automatic personal identification. . It is receiving more and more attention and is frequently used in civil applications like access control and financial security. One of the critical elements in image processing applications that refer to human traits for user verification is the fingerprint. In real terms, wrinkles will split ridgelines and create a string of false minutiae, fatally destroying the structure and texture of a fingerprint. Wrinkles will thereby decrease the accuracy of the fingerprint identification algorithm, especially in the leading technique based on minutiaematching. In this article, we investigate two elements of the treatment of wrinkles and ridge reconstruction using fingerprint photos. To start, we calculate the distance between pseudo minutiae pairing. Then, we reconstruct ridges based on midpoint criteria of a line segment with endpoints. The outcomes are presented in the paper.

References

M. NarayanMohanty and R. Sikka, "oReview on fingerprint-based identification system," Materials Today: Proceedings, 2021. [2] L. N. Darlow and B. Rosman, "Fingerprint minutiae extraction using deep learning," in 2017 IEEE International Joint Conference on Biometrics (IJCB), 2017, pp. 22-30: IEEE. [3] W. Jian, Y. Zhou, H. Liu, and N. Zhu, "Crease Detection and Repair Based on Minutia Density Distribution," in 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN), 2019, pp. 446-451: IEEE. [4] W. Bian, D. Xu, Q. Li, Y. Cheng, B. Jie, and X. Ding, "A survey of the methods on fingerprint orientation field estimation," IEEE Access, vol. 7, pp. 32644-32663, 2019. [5] R. Gupta, M. Khari, D. Gupta, and R. G. Crespo, "Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction," Information Sciences, vol. 530, pp. 201-218, 2020. [6] A. A. ABBOOD, G. SULONG, A. M. TAHA, and S. U. PETERS, "A new technique for estimating and enhancing orientation field of fingerprint image," Journal of Theoretical and Applied Information Technology, vol. 96, no. 7, 2018. [7] C. Yuan and X. Sun, "Fingerprint liveness detection using histogram of oriented gradient based texture feature," Journal of Internet Technology, vol. 19, no. 5, pp. 1499-1507, 2018

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Published

2022-09-06

How to Cite

Firas S. Abdulameer, Firas S. Abdulameer, & Kuba Hasanien Kariem. (2022). RECONSTRUCTION CREASE FOR FINGERPRINT IMAGES USING MINUTIA DENSITY DISTRIBUTION: AN EMPIRICAL STUDY. European Journal of Research Development and Sustainability, 3(9), 28-36. Retrieved from https://scholarzest.com/index.php/ejrds/article/view/2646

Issue

Vol. 3 No. 9 (2022): EJRDS

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.