Online Signature Verification: Is the Whole Greater Than the Sum of the Parts?

To choose the best features to model the signatures is one of the most challenging problems in online signature verification. In this paper, the idea is to evaluate whether it would be possible to combine different feature sets selected by different criteria in such a way that their main characteristics could be properly exploited and the verification performance could be improved with respect to the case of using each set individually. In particular, the combination of an automatically selected feature set, a feature set inspired by the ones used by Forensic Handwriting Experts (FHEs), and a set of global features is proposed. Two different fusion strategies are used to perform the combination, namely, a decision level fusion scheme and a pre-classification scheme. Experimental results show that the proposed feature combination approaches result not only in improvements regarding the verification error rates but also the simplicity, flexibility and interpretability of the verification system.