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

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