Learning Generalisable Representations for Offline Signature Verification

Current offline signature verification methods based on deep learning have achieved promising results, but these methods degrade greatly in cross-domain settings. An efficient offline signature verification model with both high performance and for deployment cross-domain without any adaptation. In this paper, we propose a novel approach to learning generalisable representations for offline signature verification. Firstly, we use the Siamese network combined with Triplet loss and Cross Entropy (CE) loss to learn discriminative features. Secondly, we introduce Instance Normalization (IN) into the network to cope with cross-domain discrepancies and propose an Inference Layer Normalization Neck (ILNNeck) module to further improve model generalization. We evalute the method on our self-collected Multilingual Signature dataset (MLSig) and three public datasets: BHSig-H, BHSig-B, and CEDAR. Results show that while our method achieves comparable results in single-domain setting, it is obviously superior to state-of-the-art methods in cross-domain setting.