Forgery-free signature verification with stroke-aware cycle-consistent generative adversarial network

• Published in: Neurocomputing (Amsterdam) 2022-10, Vol.507, p.345-357
• Main Authors: Jiang, Jiajia, Lai, Songxuan, Jin, Lianwen, Zhu, Yecheng, Zhang, Jiaxin, Chen, Bangdong
• Format: Article
• Language: English
• Subjects: Deep learning; Handwritten signature verification; Online-to-offline; Signature duplication; Style transfer
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2022.08.017

In recent years, the performance of handwritten signature verification (HSV) has been considerably improved by deep learning methods. However, deep HSV still faces significant challenges due to the lack of training data, especially for skilled forgeries. In this context, signature synthesis is a promising alternative to address the problem of insufficient data. Compared with offline modality, online signatures are more likely to produce natural duplicates by virtue of their dynamic information. Therefore, we propose a novel convolutional neural network model for offline HSV, called SigCNN, and utilize CycleGAN in style transfer fields to generate realistic offline signatures from online specimens and their duplicates. To compensate for the deficiency of vanilla CycleGAN in generating diverse stroke widths, we propose a new method, Stoke-cCycleGAN, to generate signatures at desired stroke width levels. By online signature duplication and online-to-offline conversion, our SigCNN model can be trained without requiring skilled forgeries. Experimental results showed that our SigCNN trained on generated signatures achieved competitive results on public datasets compared to existing methods. Code of Stroke-cCycleGAN is available at https://github.com/KAKAFEI123/Stroke-cCycleGAN.