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

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

Neha Tripathi

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. International Journal of Information Security Engineering. 2023; 01(01):1-15.
How to cite this URL: Neha Tripathi, Khushbu Rai. Matching Minutiae Fingerprint Q-Learning Approach for Detail Coordination: Identifiable Mark Point. International Journal of Information Security Engineering. 2023; 01(01):1-15. Available from: https://journals.stmjournals.com/ijise/article=2023/view=111473


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