A Prototypical Signature Approach for Writer-Independent Offline Signature Verification
For the signature verification community, this method addresses the problem of inefficient training due to random negative sampling, offering a backbone-agnostic solution that enhances scalability.
The paper proposes a data-driven strategy using prototypical signatures to generate diverse, informative negative samples for offline signature verification, improving skilled forgery detection and computational efficiency.
Offline handwritten signature verification aims to distinguish genuine from forged signatures using static images. Since real forgeries are rarely available, negative samples are usually randomly drawn from genuine signatures of other users to create training data. However, this random selection often lacks diversity, increases redundancy, and escalates computational cost, leading to inefficient training. We propose a data-driven strategy to generate diverse, informative negative samples using prototypical signatures, which are compact, non-identifiable summaries of genuine signature features. Based on the experiments results, we conclude that (i) prototypical signatures yield more informative negative samples, improving the detection of skilled forgeries; (ii) the proposed approach is backbone-agnostic, showing robustness across architectures; and (iii) when combined with a primal-form linear SVM, it serves as an alternative to RBF-based models while significantly improving scalability and computational efficiency. Implementation of the method is available at https://github.com/kdmoura/proto_hsv.