SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification, ICIP 2022 (Oral)

Siladittya Manna, Soumitri Chattopadhyay, Saumik Bhattacharya and Umapada Pal, "SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification", IEEE International Conference on Image Processing (ICIP), 2022.

⚡ Update: Our paper has been selected for Oral presentation! ⚡

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.

Citation

If you find this article useful in your research, consider citing us:

@inproceedings{manna2022swis,
    author = {Siladittya Manna and Soumitri Chattopadhyay and Saumik Bhattacharya and Umapada Pal},
    title = {SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification},
    booktitle = {IEEE International Conference on Image Processing (ICIP)},
    year = {2022}
}