Offline Signature Verification with Transformers

Abstract

Signature verification is a frequently-used forensics technology. Although the previous convolution neural network (CNN) based methods have made a great progress, the limitation of local neighborhood operation of CNN impedes reasoning about the relation of global signature strokes. To overcome this weakness, in this paper, we propose a novel holistic-part unified model named TransOSV based on the transformer framework. Signature images are encoded into patch sequences by the proposed holistic encoder to learn global representation. Considering the subtle local difference between the genuine signature and forged signature, we design a contrast based part decoder that is utilized to learn discriminative part features. To reduce the influence of sample imbalance, we formulate a new focal contrast loss function. Extensive experimental results and ablation studies prove the potential of the proposed model.