Multiscale Global and Regional Feature Learning Using Co-Tuplet Loss for Offline Handwritten Signature Verification

Handwritten signature verification is a significant biometric verification method widely acknowledged by legal and financial institutions. However, the development of automatic signature verification systems poses challenges due to inter-writer similarity, intra-writer variations, and the limited number of signature samples. To address these challenges, we propose a multiscale global and regional feature learning network (MGRNet) with the co-tuplet loss, a new metric learning loss, for offline handwritten signature verification. MGRNet jointly learns global and regional information from various spatial scales and integrates it to generate discriminative features. Consequently, it can capture overall signature stroke information while detecting detailed local differences between genuine and skilled-forged signatures. To enhance the discriminative capability of our network further, we propose the co-tuplet loss, which simultaneously considers multiple positive and negative examples to learn distance metrics. By dealing with inter-writer similarity and intra-writer variations and focusing on informative examples, the co-tuplet loss addresses the limitations of typical metric learning losses. Additionally, we develop HanSig, a large-scale Chinese signature dataset, to facilitate the development of robust systems for this script. The dataset is available at https://github.com/ashleyfhh/HanSig. Experimental results on four benchmark datasets in different languages demonstrate the promising performance of our method in comparison to state-of-the-art approaches.