Abstract
In this paper, an on-line signature verification system exploiting local and global information using two-stage fusion is presented. At the first stage, global information is extracted as 13-dimensional vector and recognized by majority classifiers, and then local information is extracted as time functions of various dynamic properties and recognized by BP neural network classifier. By fusing global and local information and introducing an enhanced dynamic time warping algorithm and a normalized feature measure, our method obtained an average EER of 4.02% on public database SVC2004 (first signature verification competition 2004) Task2 compared to 6.90% the first place at SVC2004.
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.
