In this work, an approach for online signature verification based on writer specific features and classifier is investigated. Existing models for online signatures are generally writer independent, as a common classifier or fusion of classifier is used on a common set of features for all writers during verification. In contrast, our approach is based on the usage writer dependent features as well as writer dependent classifier. The two decisions namely optimal features suitable for a writer and a classifier to be used for authenticating the writer are taken based on the error rate achieved with the training samples. The performance of our model is tested on both MCYT-100 (DB1), a sub corpus of MCYT data set, consisting of signatures of 100 writers, MCYT-330 (DB2) consisting of signatures of all 330 writers and visual subcorpus of SUSIG dataset. Experimental results confirm the effectiveness of writer dependent characteristics for online signature verification. The error rate that we achieved is lower when compared to many existing contemporary works on online signature verification especially when the number of training samples available for each writer is sufficient enough.
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