V Kakulapati*, Shaik Subhani, Deepthi Madireddy, Ramavath Mounika and Ananthoju Bhargavi

Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India

*Corresponding Author: V Kakulapati, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India.

Received: May 02, 2024; Published: May 10, 2024

Abstract

This biometrics study focuses on the identification of people via fingerprint recognition. Investigate the use of this kind of system. One of the most popular techniques for identifying individuals is fingerprint recognition, which is widely acknowledged. To generate an accurate model for fingerprint identification, this study uses machine learning techniques. The model is tested and trained using a dataset of genuine and highly altered fingerprints, and it achieves a 95% accuracy on the test set. The work integrates Lime (Local Interpretable Model-agnostic Explanations) for interpretability with the Histogram of Oriented Gradients (HOG) for feature extraction. Preprocessing images, training models, and a demonstration of real-time fingerprint recognition are all part of the project structure. This technique is useful in real-world scenarios for biometric authentication, safe access control systems, and forensic investigations. It provides a stable fingerprint recognition system that could enhance security and identity verification.

Keywords: Bi-metric; Lime; Finger; Security; Accuracy; Lime; HOG; Model; ML; Genuine; Imposter; Dataset

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Citation

Citation: V Kakulapati., et al. “Fingerprint Recognition Using the HoG and Lime Algorithm".Acta Scientific Computer Sciences 6.6 (2024): 15-19.

Copyright

Copyright: © 2024 V Kakulapati., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.