SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification

TL;DR

This paper introduces SWIS, a novel self-supervised learning framework for offline signature verification that effectively addresses data scarcity by learning decorrelated, writer-independent features.

Contribution

It is the first to apply self-supervised learning to offline signature verification, improving feature representation without labeled data.

Findings

Encouraging results on multiple datasets.

Effective feature decorrelation and redundancy reduction.

First application of SSL in signature verification.

Abstract

Writer independent offline signature verification is one of the most challenging tasks in pattern recognition as there is often a scarcity of training data. To handle such data scarcity problem, in this paper, we propose a novel self-supervised learning (SSL) framework for writer independent offline signature verification. To our knowledge, this is the first attempt to utilize self-supervised setting for the signature verification task. The objective of self-supervised representation learning from the signature images is achieved by minimizing the cross-covariance between two random variables belonging to different feature directions and ensuring a positive cross-covariance between the random variables denoting the same feature direction. This ensures that the features are decorrelated linearly and the redundant information is discarded. Through experimental results on different data sets, we obtained encouraging results.