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Abstract-Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection, which has enormous impact on the accuracy of signature verification. This paper presents a method based on self-taught learning, in which a sparse autoencoder attempts to learn discriminative features of signatures from a large unlabeled signature dataset. Then, the features learned are employed to present users' signatures by creating a model for each user based on user genuine signatures. Finally, users' signatures are classified using a one-class classifier. The proposed method is independent on signature datasets thanks to self-taught learning. The features have been learned from 17,500 signatures (ATVS dataset) and verification process of the proposed system is evaluated on SVC2004 and SUSIG signature datasets, which contain genuine and skilled forgery signatures. The experimental results indicate significant error reduction and accuracy enhancement in comparison with state of the art counterparts.

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