Optimal feature set selection in online signature verification

Abstract

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.