Online Signature Verification using Deep Representation: A new Descriptor
This work improves signature verification accuracy for security applications, but it is incremental as it builds on existing deep learning and one-class classification techniques.
The paper tackles online signature verification by addressing limited training samples and spatial invariance, proposing a method that uses a pre-trained sparse auto-encoder for feature learning and one-class classifiers per user, resulting in significant error reduction and accuracy enhancement on SVC2004 and SUSIG datasets.
This paper presents an accurate method for verifying online signatures. The main difficulty of signature verification come from: (1) Lacking enough training samples (2) The methods must be spatial change invariant. To deal with these difficulties and modeling the signatures efficiently, we propose a method that 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 unlabeled signatures, then we applied the discriminative features, which are learned by auto-encoder to represent the training and testing signatures as a self-thought learning method (i.e. we have introduced a signature descriptor). Finally, user's signatures are modeled and classified using a one-class classifier. The proposed method is independent on signature datasets thanks to self-taught learning. The experimental results indicate significant error reduction and accuracy enhancement in comparison with state-of-the-art methods on SVC2004 and SUSIG datasets.