Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
The online signature verification has attracted many researchers in recent past as it offers useful real life applications. This paper presents role of four types of feature sets as static, kinematics, structural and statistical in nature and these feature sets are analysed in context of online signature verification. The signatures are verified as single trajectory and in combination of multiple sub-trajectories. We have applied feature sets with all possible permutations to signature trajectory and sub-trajectories. We have computed a total of 80 features and categorised to four feature sets on the basis of their behavioural characteristics. The inter-valued symbolic representation technique has been used to clearly understand the impact of each individual feature set or in combinations of feature set. The simulation results are presented using popular benchmark dataset SVC 2004 where both sub-datasets as TASK1 and TASK2 are used. The experimental results show that it is a promising correlation between different feature sets and suggest the optimal combination among several combinations of feature sets.
International Journal of Biometrics – Inderscience Publishers
Published: Jan 1, 2017
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.