Multi-region minutiae depth value-based efficient forged finger print analysis

PLOS ONE RESEARCH ARTICLE Multi-region minutiae depth value-based efficient forged finger print analysis M. Baskar ID1, Renuka Devi Rajagopal2, PRASAD B. V. V. S.3, J. Chinna Babu ID4*, Gabriela Pajtinková Bartáková5, T. S. Arulananth6 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Baskar M, Rajagopal RD, B. V. V. S. P, Babu JC, Bartáková GP, Arulananth TS (2023) Multi-region minutiae depth value-based efficient forged finger print analysis. PLoS ONE 18(11): e0293249. https://doi.org/10.1371/journal. pone.0293249 Editor: Bhisham Sharma, Chitkara University, INDIA Received: August 19, 2023 Accepted: October 9, 2023 Published: November 16, 2023 Copyright: © 2023 Baskar 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. Data Availability Statement: All relevant data are within the paper and Supporting Information. Funding: The authors extend their appreciation to Gabriela Pajtinková Bartákova, Faculty of Management, Comenius University in Bratislava, Odbojárov 10, 82005 Bratislava 25, Slovakia for providing the funding for this Manuscript. Competing interests: The authors have declared that no competing interests exist. 1 Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu, India, 2 School of Computer Science and Engineering, VIT University, Chennai, Tamilnadu, India, 3 School of Engineering (CSE), Anurag University, Hyderabad, India, 4 Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, AP, India, 5 Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia, 6 Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, India * Abstract The application of biometrics has expanded the wings to many domains of application. However, various biometric features are being used in different security systems; the fingerprints have their own merits as it is more distinct. A different algorithm has been discussed earlier to improve the security and analysis of fingerprints to find forged ones, but it has a deficiency in expected performance. A multi-region minutiae depth value (MRMDV) based finger analysis algorithm has been presented to solve this issue. The image that is considered as input has been can be converted into noisy free with the help of median and Gabor filters. Further, the quality of the image is improved by sharpening the image. Second, the preprocessed image has been divided into many tiny images representing various regions. From the regional images, the features of ridge ends, ridge bifurcation, ridge enclosure, ridge dot, and ridge island. The multi-region minutiae depth value (MRMDV) has been computed based on the features which are extracted. The test image which has a similarity to the test image is estimated around MRMDV value towards forgery detection. The MRMDV approach produced noticeable results on forged fingerprint detection accuracy up to 98% with the least time complexity of 12 seconds. 1. Introduction Various organizations have used the development of information technology to meet their goals. As the organizations have a variety of information on their system, which belongs to different users and business partners, they are responsible for securing the data most effectively. Any organization faces various challenges against the data maintained through threats. The security measures which can be different are enforced to secure the data and handle the problem of illegal access. Access restriction is the most dominant one, which restricts the illegal user from accessing the available data. In this way, different approaches are used, like profile- PLOS ONE | https://doi.org/10.1371/journal.pone.0293249 November 16, 2023 1 / 16 PLOS ONE Multi-Region Minutiae Depth Value-Based Efficient Forged Finger Print Analysis based access and key-based access restriction methods. However, the performance of such methods is not efficient in meeting the system’s security requirements as they can be tampered with easily by various adversaries. Using biological features is more effective in enforcing such security systems. The facial features and thumb features are more challenging for the adversary that can support such security systems. Fingerprints and palm prints can be used towards the problem effectively. Human fingerprint has great independence among other features of biometrics. It has unique characteristics which vary between any number of users. It has components of Minutiae ending, bifurcation, islands, dots, and so on. These components can be common in all human fingerprints but vary in numbers and sizes. The components and their numbers can be obtained by processing the fingerprint image. These numbers will not correlate with any other numbers. So, by adopting such finger analysis in security systems, the performance of authentication and illegal access restriction can be enforced most strictly. The picture of the sample fingerprint is presented in Fig 1, which has both original and altered fingerprints. The adversary or malformed user would try to breach the security walls by producing an altered print to the system. However the system should be capable of differentiating the original and altered one. So, the security system should consider various features from the ridge like dots, islands, ends, enclosure, and bifurcation. By considering such features in the authentication and verification process, the problem of forgery detection can be handled effectively. Adapting finger print analysis to organizational security is a highly required process. Because finger print is the most unique feature for any human, and by including such feature for security reasons improves the security of any organization. However, there are adversaries who would produce forged finger print for the system and the system should be more rigid in detecting such forged finger prints. If the system is highly efficient in detecting forged finger prints then the security performance of the system will be highly efficient. This would be used in many security applications from banks to the defense sector. This score presents an efficient Multi Region Minute Depth Value-Based Efficient Forged Finger Print Analysis model in this article. The model considers the depth of each feature in different regions to perform the verification process. The model estimates the Minutiae Depth Value (MDV) according to the number of features present and their instances with the mass value. By computing such MDV values for various regions, the method computes MRMDV values to classify the fingerprint. The multi-regio (...truncated)