Abstract
Finger vein and finger-knuckle-print have been studied for personal identification. Methods utilizing direction and location of finger veins have achieved promising performance. However, it is sensitive to quality of finger vein images and it is slow. In this paper, we develop a fast and robust algorithm for person recognition using a coarse-to-fine classifier. 2DPCA is used for coarse selection of k nearest candidates. To increase the robustness of the algorithm, a candidate person is selected when either its finger vein or finger-knuckle-print is near the corresponding test sample. Competitive coding schema is then conducted on the n (n <= 2k) candidates and test images for final classification. Experimental results show that our method is faster and achieved comparable recognition rate with state-of-the-art methods.
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.
