Fingerprints are widely recognized as one of the most unique and reliable characteristics of human identity, making them a preferred choice for biometric-based authentication systems. To use of contactless fingerprints has emerged as an alternative. This paper focuses on the development of a deep learning-based segmentation tool for contactless fingerprint localization and segmentation. Our system leverages deep learning techniques to achieve high segmentation accuracy and reliable extraction of fingerprints from contactless fingerprint images. In our evaluation, our segmentation method demonstrated an average mean absolute error (MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a labeling accuracy of 97.46%. These results demonstrate the effectiveness of our novel contactless fingerprint segmentation and extraction tools.
Murshed et al. (Wed,) studied this question.