Abstract
In this study, a novel method based on deep learning for aligned fingerprints matching is proposed. According to the characteristics of fingerprint images, a convolutional network, Finger ConvNet, is designed. In addition, a new joint supervision signal is used to train Finger ConvNet to obtain deep features. Experimental studies are performed on public fingerprint datasets, the ID Card fingerprint dataset and the Ten-Finger Fingerprint Card fingerprint dataset. Furthermore, four performance indicators, the false matching rate (FMR), false non-matching rate (FNMR), equal error rate (EER) and receiver operating characteristic (ROC) curve, are measured. The experimental results demonstrate the effectiveness of the proposed method, which achieved a competitive effect in comparison with conventional fingerprint matching algorithms in fingerprint verification tasks using the FVC2000, FVC2002, and FVC2004 datasets. Moreover, the matching speed of the proposed method was almost 5 times faster than the fastest conventional fingerprint matching algorithms. In addition, it can be used as a fast matching method to filter out many templates with low scores by setting a threshold according to the matching scores and thus accelerate the process in identification tasks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
