Paired contrastive feature for highly reliable offline signature verification

Xiaotong Ji, Daiki Suehiro, Seiichi Uchida

Abstract: Highlights•We propose a novel concept of Paired Contrastive Feature (PCF) for highly reliable writer-independent signature verification by converting a task with pairwise contrastive evaluation into a task with sample-wise evaluation.•Two highly reliable machine-learning frameworks, top-rank learning and learning with rejection, are applied to the writer-independent signature verification task for the first time by constructing PCF to the authors’ best knowledge.•We validated the reliability and effectiveness of the proposed PCF with highly reliable methods through experiments with multiple evaluation metrics.