Fusing deep and hand-crafted features by deep canonically correlated contractive autoencoder for offline signature verification

Abstract

Handwritten signatures are currently the most widely used and recognized form of identity authorization, which is a significant way for individuals to express their identity to information. Since the forgers learn information about the genuine signatures from the target signer in advance, there are usually only minor discrepancies between skilled forged and genuine signatures. Therefore, building an automatic handwritten signature verification system to recognize skilled forgeries is a worthy challenging task. In this paper, to learn a good representation for distinguishing skilled forged and genuine signatures, we propose an offline handwritten signature verification system that fuses deep learning-based and hand-crafted features, which combines the merits of different views of features. Specifically, a novel multi-view representation learning method is proposed, named Deep Canonically Correlated Contractive Autoencoder (DCCCAE) for learning combined representations between deep and hand-crafted features. After the feature learning process, we train Support Vector Machines (SVMs) as writer-dependent classifiers for each signer to build the completed verification system. Extensive experiments and analyses on four different language datasets, such as English (CEDAR), Persian (UTSig), Bengali and Hindi (BHSig), and Chinese (SigComp2011) demonstrate that the proposed system improves the learning ability compared with the single view features and achieve the competitive performance compared with the state-of-the-art verification systems.