An Efficient Online Signature Verification Based on Feature Fusion and Interval Valued Representation of Writer Specific Features

Online Signature Verification (OSV) is a pattern recognition problem, which involves analysis of discrete-time signals of signature samples to classify them as genuine or forgery. One of the core difficulties in designing online signature verification (OSV) system is the inherent intra-writer variability in genuine handwritten signatures, combined with the likelihood of close resemblances and dissimilarities of skilled forgeries with the genuine signatures. To address this issue, in this manuscript, we emphasize the concept of writer dependent parameter fixation (i.e. features, decision threshold and feature dimension) using interval valued representation grounded on feature fusion. For an individual writer, a subset of discriminative features is selected from the original set of features using feature clustering techniques. This is at variance with the writer independent models in which common features are used for all the writers. To practically exhibit the efficiency of the proposed model, thorough experiments are carried out on benchmarking online signature datasets MCYT-100 (DB1), MCYT-330 (DB2) consist of signatures of 100, 330 individuals respectively. Experimental result confirms the efficiency of writer specific parameters for online signature verification. The EER value, the model computes, is lower compared to various latest signature verification models.