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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\u2019s robust capability to effectively differentiate between genuine fingers and fake replicas.<\/jats:p>","DOI":"10.1007\/s10586-025-05829-5","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:38:55Z","timestamp":1763566735000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning based contactless fingerprint identification"],"prefix":"10.1007","volume":"29","author":[{"given":"Mohammad","family":"Alsmirat","sequence":"first","affiliation":[]},{"given":"M. Moneb","family":"Khaled","sequence":"additional","affiliation":[]},{"given":"Aghyad A. 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