Open Access
ITM Web Conf.
Volume 43, 2022
The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
Article Number 01014
Number of page(s) 10
Published online 14 March 2022
  1. Meghna B. Patel, Satyen M. Parikh, Ashok R. Patel Performance Improvement in Preprocessing Phase of Fingerprint Recognition, Information and Communication Technology for Intelligent Systems pp 521-530. 15, December 2018. [Google Scholar]
  2. Ramesh Chandra Sahoo, Sateesh Kumar Pradhan, A Broad Survey On Feature Extraction Methods For Fingerprint Image Analysis, International Journal of Computer Engineering and Technology (IJCET) Volume 10, Issue 2, pp. 1424, Article ID: IJCET-10-02-002, March-April 2019. [Google Scholar]
  3. Arucha Rungchokanun, Watcharapong Chaidee, Chonlatid Deerada, Vutipong Areekul, Effect of Pre-Enhancement on False-Rejection Cases of Fingerprint Verification System, 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 04 August 2020. [Google Scholar]
  4. Anna Czech, Aleksandra Szabelak and Artur Sowinski Changes in Fingerprints Depending on Physiological Factors, Paper Criminalistics, Journal of forensic science, Volume 64, issue3,, May 2019. [Google Scholar]
  5. A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, An identity authentication system using fingerprints, IEEE Trans. PAMI, vol. 85, no. 9, pp. 1365-1388, Sept 1997. [Google Scholar]
  6. Shalash, W.M, Abou-Chadi, F.E.Z.: Fingerprint image enhancement with dynamic block size. In: 23rd IEEE National Radio Science Conference, vol. 0, pp. 1–8, 2006. [Google Scholar]
  7. A.M. Tahmasebi, S. Kasaei, A novel adaptive approach to fingerprint enhancement filter design, Signal Processing: Image Communication 17, pp. 849-855, 2002. [CrossRef] [Google Scholar]
  8. Neethu S, Sreelakshmi S, Deepa Sankar (2014) ’Enhancement of fingerprint using FFT×|FFT| nfilter’, Procedia Computer Science, Volume 46, 2015, Pages 1561-1568. [CrossRef] [Google Scholar]
  9. Lukasz Wieclaw, Gradient Based Fingerprint Orientation Field Estimation, Journal of Medical Informatics and Technologies Vol. 22/2013, ISSN 1642-6037. 2013. [Google Scholar]
  10. Chen, Y., Yu, Y.: Thinning approach for noisy digital patterns. Pattern Recogn. (Elsevier) 29(11), 1847–1862 (1996). [CrossRef] [Google Scholar]
  11. Bradley, D., G. Roth, “Adapting Thresholding Using the Integral Image,” Journal of Graphics Tools. Vol. 12, No. 2, pp.13–21, 2007. [CrossRef] [Google Scholar]
  12. Lam, L., Seong-Whan Lee, and Ching Y. Suen, “Thinning Methodologies-A Comprehensive Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 14, No. 9, September 1992, page 879, bottom of first column through top of second column. [Google Scholar]
  13. Champod, C., Chamberlain, P.: Fingerprints, Chap. 3. Routledge 2009. [Google Scholar]
  14. Shahida Jabeen, Shoab Ahmed Khan, “A hybrid false minutiae removal algorithm with boundary elimination”,June 2008,DOI: 10.1109/SYSOSE.2008.4724177. [Google Scholar]
  15. Maltoni, D., Maio, D., Jain, A.K. and Prabhakar S. (2003) Handbook of Fingerprint Recognition, Springer-Verlag. [Google Scholar]
  16. Neusius, C. and Olszewski J. (1994) ‘A noniterative thinning algorithm’, ACM Transactions on Mathematical Software, Vol. 20, No. 1, pp.5–20. [CrossRef] [MathSciNet] [Google Scholar]
  17. Mouad. M.H. Ali ; Vivek H. Mahale ; Pravin Yannawar, “Fingerprint Recognition for Person Identification and Verification Based on Minutiae Matching”, 2016 IEEE 6th International Conference on Advanced Computing (IACC). 10.1109/IACC.2016.69. August 2016. [Google Scholar]
  18. Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognition. 28(11), 1657–1672 (1995). [CrossRef] [Google Scholar]
  19. Institute of Standards and Technology, (accessed on: 12/05/2015) [Google Scholar]
  20. [Google Scholar]
  21. R. Anandha Jothi, J. Nithyapriya, V. Palanisamy, Performance Improvement in Fingerprint Feature Extraction Using Minutiae Local Triangle Feature Set, DOI: 10.1109/IMICPW.2019.8933166, 2019 TEQIP III Sponsored International Conference on Microwave Integrated Circuits, Photonics and Wireless Networks (IMICPW), December 2019. [Google Scholar]
  22. Greevar et al. “Pre-and Post-fingerprint Skeleton Enhancement for Minutiae Extraction”, Proceedings of International Conference on Computer Vision and Image Processing, Advances in Intelligent Systems and Computing 459, 1(2017) 453-465. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.