Title: Latent fingerprint enhancement and matching using intuitionistic type-2 fuzzy

Authors: Sayima Mukhtar; Mohsin Furkh Dar; Amandeep Kaur

Addresses: Artificial Intelligence Lab, School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, Telangana, India ' Artificial Intelligence Lab, School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, Telangana, India ' Department of Computer Sciences, Central University Punjab, Bathinda, 151001, Punjab, India

Abstract: Latent fingerprints are obtained from crime places by law enforcement and forensic agencies to identify the suspect. The latent fingerprints have vague ridge structures and various overlapping valley-based structures that result in low image quality. Furthermore, background noise, low contrast, and low information content make feature extraction difficult. To address these challenges, we propose a novel intuitionistic type-2 fuzzy relation for enhancing image quality. And to improve the matching score, a crossing number method is applied to extract minutiae points. The matching score is calculated using four different distance algorithms: Manhattan distance, Euclidean distance, Earth mover's distance, and chi-square distance. The proposed methodology is evaluated using three datasets IIITD, FVC-2004-1, and FVC-2004-2. The experimental results show that the proposed method yields satisfactory results, with the chi-square distance achieving the highest accuracy. The proposed method can be used by law enforcement and forensic agencies to reduce the workload.

Keywords: latent fingerprint image; fuzzy set; similarity score; Manhattan distance; chi-square distance; Earth mover's distance; EMD.

DOI: 10.1504/IJAISC.2022.130558

International Journal of Artificial Intelligence and Soft Computing, 2022 Vol.7 No.4, pp.313 - 328

Received: 19 Sep 2022
Received in revised form: 10 Dec 2022
Accepted: 18 Dec 2022
Published online: 27 Apr 2023 *

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