Abstract
Online handwritten signature takes in consideration x, y, z coordinates as well as pressure, azimuth and altitude of the pen tip. In this paper, Hybrid Wavelet Transform I and II based feature vector are extracted from the online signature and then they are given to Hidden Markov Model based classifier. Various combinations of Feature Vector Elements and samples are tested and the performance of the system is analyzed. Hybrid wavelet transform was applied on the first 128 samples of the pressure parameter and 1- 16 and 33- 64 samples of the output were used as feature vector for signature verification. Using Hidden Markov Model (HMM) classifier the performance was compared. For 1-16 samples KEKRE 128 offers best performance of FRR 1% and FAR 1 %. For 33-64 samples, KEKRE HAAR and HADAMARD HAAR offers best performance with FRR 1% and FAR 1%. 1-16 samples offers better FRR-FAR than 33-64 samples. For 1–16 samples HADAMARD DHT offers best performance of AAR 28% and ARR 28 %. For 33-64 samples, DHT 128 offers best performance with AAR 58% and ARR 29%. 33-64 samples offers better AAR-ARR than 1-16 samples.
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.
