  Offline Signature Verification (OSV) is a fundamental biometric task across
various forensic, commercial and legal applications. The underlying task at
hand is to carefully model fine-grained features of the signatures to
distinguish between genuine and forged ones, which differ only in minute
deformities. This makes OSV more challenging compared to other verification
problems. In this work, we propose a two-stage deep learning framework that
leverages self-supervised representation learning as well as metric learning
for writer-independent OSV. First, we train an image reconstruction network
using an encoder-decoder architecture that is augmented by a 2D spatial
attention mechanism using signature image patches. Next, the trained encoder
backbone is fine-tuned with a projector head using a supervised metric learning
framework, whose objective is to optimize a novel dual triplet loss by sampling
negative samples from both within the same writer class as well as from other
writers. The intuition behind this is to ensure that a signature sample lies
closer to its positive counterpart compared to negative samples from both
intra-writer and cross-writer sets. This results in robust discriminative
learning of the embedding space. To the best of our knowledge, this is the
first work of using self-supervised learning frameworks for OSV. The proposed
two-stage framework has been evaluated on two publicly available offline
signature datasets and compared with various state-of-the-art methods. It is
noted that the proposed method provided promising results outperforming several
existing pieces of work. The code is publicly available at:
https://github.com/soumitri2001/SURDS-SSL-OSV
