Matching Minutiae Fingerprint Q-Learning Approach for Detail Coordination: Identifiable Mark Point - Neha Tripathi; Khushbu Rai (International Journal of Information Security Engineering, 2023)
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; convolution neutral network (CNN); deep learning (DL); minutiae extraction; principal component analysis (PCA); support vector machine (SVM)