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This paper presents an accurate method for verifying online signatures. Themain difficulty of signature verification come from: (1) Lacking enoughtraining samples (2) The methods must be spatial change invariant. To deal withthese difficulties and modeling the signatures efficiently, we propose a methodthat a one-class classifier per each user is built on discriminative features.First, we pre-train a sparse auto-encoder using a large number of unlabeledsignatures, then we applied the discriminative features, which are learned byauto-encoder to represent the training and testing signatures as a self-thoughtlearning method (i.e. we have introduced a signature descriptor). Finally,user's signatures are modeled and classified using a one-class classifier. Theproposed method is independent on signature datasets thanks to self-taughtlearning. The experimental results indicate significant error reduction andaccuracy enhancement in comparison with state-of-the-art methods on SVC2004 andSUSIG datasets.

Online Signature Verification using Deep Representation: A new Descriptor