Matching Minutiae Fingerprint Q-Learning Approach for Detail Coordination: Identifiable Mark Point

Year : 2023 | Volume : 01 | Issue : 01 | Page : 1-15
By

    Neha Tripathi

  1. Khushbu Rai

  1. Student, Computer Science and Engineering, Lakshmi Narayan College of Technology and Science, Madhya Pradesh, India
  2. Professor, Computer Science and Engineering Lakshmi Narayan College of Technology and Science, Madhya Pradesh, India

Abstract

The use of fingerprints for high-precision recognition and identification of people is one of the most reliable biometric symbols because it is non-invasive. In this paper, we propose an innovative approach to detect details on low contrast resolution image quality of fingerprint images. Existing algorithms are not very susceptible to sound and image excellence due to the lack of level of intensity. We recommend a reliable route to find fingerprints and then they can employ machine learning techniques to enhance image quality and choose the best course of action. For a sizable portion of the state, multi-layer concepts and intensive learning techniques are used before choosing the proper reward structure and research area for understanding the distribution of rewards. It is of great importance that the opportunities for the development of the content are easy and educational activities are of great importance. Experimental outcomes test indicates that the best way to extract Q-Learning details is to use a contemporary finger recognition system framework, which offers extremely viable or even significantly better results may generate than several cutting-edge techniques in terms of AROC, accuracy, and other metrics.

Keywords: Machine learning, minutiae extraction, convolution neutral network (CNN), support vector machine (SVM), principal component analysis (PCA), deep learning (DL)

[This article belongs to International Journal of Information Security Engineering(ijise)]

How to cite this article: Neha Tripathi, Khushbu Rai Matching Minutiae Fingerprint Q-Learning Approach for Detail Coordination: Identifiable Mark Point ijise 2023; 01:1-15
How to cite this URL: Neha Tripathi, Khushbu Rai Matching Minutiae Fingerprint Q-Learning Approach for Detail Coordination: Identifiable Mark Point ijise 2023 {cited 2023 Jun 22};01:1-15. Available from: https://journals.stmjournals.com/ijise/article=2023/view=111473

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References

  1. Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of Fingerprint Recognition. New York, NY: Springer; 2009.
  2. Cappelli R, Ferrara M. A fingerprint retrieval system based on level-1 and level-2 features. Expert Syst Appl. 2012; 39 (12): 10465–10478.
  3. Pankanti S, Prabhakar S, Jain AK. On the individuality of fingerprints. IEEE Trans Pattern Anal Mach Intell. 2002; 24 (8): 1010–1025.
  4. Zhao Q, Jain AK. On the utility of extended fingerprint features: a study on pores. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition – Workshops, June 13, 2010. pp. 9–16.
  5. Nogueira RF, de Alencar Lotufo R, Machado RC. Evaluating software-based fingerprint liveness detection using convolutional networks and local binary patterns. In: 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings, October 17, 2014. pp. 22–29.
  6. Wu CP, Li WC. Three-dimensional static analysis of nanoplates and graphene sheets by using Eringen’s nonlocal elasticity theory and the perturbation method. Computers Mater Continua. 2016; 52 (2): 73–103.
  7. Jain AK, Chen Y, Demirkus M. Pores and ridges: high-resolution fingerprint matching using level 3 features. IEEE Trans Pattern Anal Mach Intell. 2006; 29 (1): 15–27.
  8. Lee W, Cho S, Choi H, Kim J. Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners. Expert Syst Appl. 2017; 87: 183–198.
  9. Zhang F, Xin S, Feng J. Combining global and minutia deep features for partial high-resolution fingerprint matching. Pattern Recogn Lett. 2019; 119: 139–147.
  10. Khodadoust J, Khodadoust AM. Fingerprint indexing based on minutiae pairs and convex core point. Pattern Recogn. 2017; 67: 110–126.
  11. Galar M, Derrac J, Peralta D, Triguero I, Paternain D, Lopez-Molina C, García S, Benítez JM, Pagola M, Barrenechea E, Bustince H. A survey of fingerprint classification part I: taxonomies on feature extraction methods and learning models. Knowledge-Based Syst. 2015; 81: 76–97.
  12. Zhang P, Li C, Hu J. A pitfall in fingerprint features extraction. In: 2010 IEEE 11th International Conference on Control Automation Robotics & Vision, December 7, 2010. pp. 13–18.
  13. Zhang D, Liu F, Zhao Q, Lu G, Luo N. Selecting a reference high resolution for fingerprint recognition using minutiae and pores. IEEE Trans Instrum Meas. 2010; 60 (3): 863–871.
  14. Ismaeil AM. Fingerprint Image Quality Analysis and Enhancement Using Fuzzy Logic Technique. Doctoral Dissertation. Khartoum, Sudan: Sudan University of Science and Technology; 2017.
  15. Zhang F, Xin S, Feng J. Combining global and minutia deep features for partial high-resolution fingerprint matching. Pattern Recogn Lett. 2019; 119: 139–147.
  16. Choi HY, Jang HU, Kim D, Son J, Mun SM, Choi S, Lee HK. Detecting composite image manipulation based on deep neural networks. In: 2017 IEEE International Conference on Systems, Signals and Image Processing (IWSSIP), May 22, 2017. pp. 1–5.
  17. Sripavithra S, Akalya S, Rajarajeswari S, Kannan J, Sumathi N. Fingerprint detection using minutiae extraction. Int J Res Appl Sci Eng Technol. 2017; 5 (III): 672–675.
  18. Bakheet S, Al-Hamadi A. Hand gesture recognition using optimized local Gabor features. J Comput Theor Nanosci. 2017; 14 (3): 1380–1389.
  19. Dabouei A, Kazemi H, Iranmanesh SM, Dawson J, Nasrabadi NM. Fingerprint distortion rectification using deep convolutional neural networks. In: 2018 IEEE International Conference on Biometrics (ICB), February 20, 2018. pp. 1–8.

Regular Issue Subscription Review Article
Volume 01
Issue 01
Received June 13, 2023
Accepted June 16, 2023
Published June 22, 2023