Document Type
Article
Abstract/Description
Biometric authentication systems, particularly contactless fingerprint methods, offer enhanced security and convenience across various domains like access control, law enforcement, and finance. Despite these advantages, contactless systems face significant challenges related to image quality, finger orientation, and environmental factors. To address this, our paper presents the first extensive deep learning-based study on contactless fingerprint recognition using a large dataset of 2,143 images from 175 individuals. Our proposed approach integrates state-of-the-art preprocessing techniques with deep learning models to boost identification performance. After studying various transfer learning models, we achieved a high accuracy of 93.5%. We also conducted two further studies on inference time and spoofing resistance. To mitigate the prolonged processing time from complex preprocessing, we propose a novel YOLOv8-based architecture that significantly reduces inference duration. For spoofing, the model was tested with fingerprint captures from screens and printed paper, demonstrating a very low accuracy. This proves our model’s robust capability to effectively differentiate between genuine fingers and fake replicas.
Department
Computer Science and Information Systems
First Page
39
DOI
https://doi.org/10.1007/s10586-025-05829-5
Volume
29
Issue
1
ISSN
1573-7543
Date
10-23-2025
Citation Information
Alsmirat, Mohammad; Khaled, M. Moneb; and Sayadi, Aghyad A.L., "Deep Learning Based Contactless Fingerprint Identification" (2025). Faculty Publications. 243.
https://lair.etamu.edu/cose-faculty-publications/243
