Point-to-Set Similarity Based Deep Metric Learning for Offline Signature Verification

Abstract

Offline signature verification is a challenging task, where the scarcity of the signature data per writer makes it a few-shot problem. We found that previous deep metric learning based methods, whether in pairs or triplets, are unaware of intra-writer variations and have low training efficiency because only point-to-point (P2P) distances are considered. To address this issue, we present a novel point-to-set (P2S) metric for offline signature verification in this paper. By dividing a training batch into a support set and a query set, our optimization goal is to pull each query to its belonging support set. To further strengthen the P2S metric, a hard mining scheme and a margin strategy are introduced. Experiments conducted on three datasets show the effectiveness of our proposed method.