CBCapsNet: A novel writer-independent offline signature verification model using a CNN-based architecture and capsule neural networks

Abstract

Offline Signature verification is a biometric method with important applications in financial, legal and administrative procedures. The verification process includes comparing the extracted features of a questioned signature with those of genuine signatures of a certain individual. There are many challenges in designing offline signature verification as dynamic temporal features of signatures are not available. Deep Convolutional Neural Networks (DCNNs) have the great capability of extracting features from signature images. Despite the important advantages of these networks, they are unable to recognize the spatial properties of each feature in a signature. In addition, max-pooling layers usually eliminate some features that are crucial for forgery detection. In this paper, we propose a novel signature verification model with a combination of a CNN and Capsule Neural Networks (CapsNet) in order to capture spatial properties of signature features, improve the feature extraction phase, and reduce the complexity of the network. Moreover, we designed a new training mechanism in which a single network is trained simultaneously by two images at the same level so that the training parameters are reduced by half. Such mechanism does not require two separate networks for learning the features. Finally, a composite backbone architecture is presented with the hybrid of the proposed CNN-CapsNet models which we name CBCapsNet. The evaluation results demonstrate that our proposed model can improve accuracy and outperform prevalent signature verification methods in the community.