Title: Human fingerprint identification using minutiae extraction algorithm

Authors: Saroj Pandey; Ankit Kumar; Kamred Udham Singh; Anurag Sinha; Panduraju Pagidimalla; Kajal Kaul; Amarnath Singh

Addresses: Department of Computer Engineering and Applications, GLA University, Mathura 281406, India ' Department of Computer Engineering and Applications, Guru Ghasidas Vishwavidalay, Bilaspur, Chattisgarh, India ' Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India ' Department of Computer Science and Information Technology, IGNOU, New Delhi, India ' Koneru Lakshmaiah Education Foundation, Hyderabad, India ' Department of IT, Bharati Vidhyapeeth, New Delhi, India ' Department of Mechanical Engineering, RKDF University, India

Abstract: Recent years have seen fingerprints emerge as the most reliable form of identification due to their distinctiveness and longevity. Because of this, fingerprint authentication is being used extensively in many different applications. Using an image processing approach, fingerprint identification is the process of verifying an individual's identity through analysis of their fingerprints. This process involves matching and enhancing the image quality. An image gathering system, image processing, and verification and detection are the three fundamental parts of a traditional fingerprint identification system. Back-propagation neural network, robust thin-plate spline (RTPS), minutiae matching method, and reference auto-correction utilising FCM-CBIR strategy is a few of the fingerprint detecting systems that have been discussed. This paper's goal is to analyse and summaries several fingerprint recognition techniques. A detailed study of the methodologies stated above is undertaken, and the minutiae matching algorithm is used for fingerprint recognition. The research is given as a flowchart, which can be expanded in future work. The false match rate (FMR) and false non-match rate (FNMR) of this approach are shown, reflecting the degree of similarity and dissimilarity with other fingerprints in the dataset.

Keywords: fingerprint identification; RTPS; FCM-CBIR; biometric; histogram equalisation.

DOI: 10.1504/IJSEM.2026.155283

International Journal of Services, Economics and Management, 2026 Vol.17 No.4, pp.469 - 486

Received: 16 Jun 2023
Accepted: 06 Dec 2023
Published online: 30 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article