Using empirical mode decomposition and Kullback-Leibler distance for online handwritten signature verification Online publication date: Thu, 26-Dec-2019
by Toufik Hafs; Layachi Bennacer; Hanene Brahmia; Mohamed Boughazi; Amine Nait-Ali
International Journal of Applied Pattern Recognition (IJAPR), Vol. 6, No. 1, 2019
Abstract: The signature is one of the most accepted biometric modalities in the world. In this paper, we present a new method for online handwritten signature based on the empirical mode decomposition (EMD). After extracting each of the signature coordinates, a phase of preprocessing and normalisation is carried out. Then, the features of the signatures are extracted by using the EMD. After that, three similarity measures are used to match the signatures between them. The database used in our work is the one used in SVC (2004). Experimental results confirm the effectiveness of our approach and show the level of its reliability. Finally, the proposed method gives an EER of 2.03% and allows high rates of recognition compared to other approaches.
Online publication date: Thu, 26-Dec-2019
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Applied Pattern Recognition (IJAPR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org