Abstract
A deep learning-based finger-vein identification system is introduced to detect fingers’ rotational and translational movements as well as interference from external lights. In addition, the effects of multiple preprocessing methods and the system’s effectiveness are investigated. The results demonstrate that the proposed system’s higher identification accuracy is 98.1 % for the SDUMLA-HMT public database. Finally, the system’s high accuracy and stability, and contactless applications indicate its practicality in midst of the COVID-19 pandemic.
Sign-in/Register to access full text options 
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.
