Paired contrastive feature for highly reliable offline signature verification.

• 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. [Display omitted] Signature verification requires high reliability. Especially in the writer-independent scenario with the skilled-forgery-only condition, achieving high reliability is challenging but very important. In this paper, we propose to apply two machine learning frameworks, learning with rejection and top-rank learning, to this task because they can suppress ambiguous results and thus give only reliable verification results. Since those frameworks accept a single input, we transform a pair of genuine and query signatures into a single feature vector, called Paired Contrastive Feature (PCF). PCF internally represents similarity (or discrepancy) between the two paired signatures; thus, reliable machine learning frameworks can make reliable decisions using PCF. Through experiments on three public signature datasets in the offline skilled-forgery-only writer-independent scenario, we evaluate and validate the effectiveness and reliability of the proposed models by comparing their performance with a state-of-the-art model. [ABSTRACT FROM AUTHOR]