Static-Dynamic Interaction Networks for Offline Signature Verification
Keywords:Biometrics, Face, Gesture & Pose, Biometrics, (Deep) Neural Network Algorithms, Security
AbstractOffline signature verification is a challenging issue that is widely used in various fields. Previous approaches model this task as a static feature matching or distance metric problem of two images. In this paper, we propose a novel Static-Dynamic Interaction Network (SDINet) model which introduces sequential representation into static signature images. A static signature image is converted to sequences by assuming pseudo dynamic processes in the static image. A static representation extracting deep features from signature images describes the global information of signatures. A dynamic representation extracting sequential features with LSTM networks characterizes the local information of signatures. A dynamic-to-static attention is learned from the sequences to refine the static features. Through the static-to-dynamic conversion and the dynamic-to-static attention, the static representation and dynamic representation are unified into a compact framework. The proposed method was evaluated on four popular datasets of different languages. The extensive experimental results manifest the strength of our model.
How to Cite
Li, H., Wei, P., & Hu, P. (2021). Static-Dynamic Interaction Networks for Offline Signature Verification. Proceedings of the AAAI Conference on Artificial Intelligence, 35(3), 1893-1901. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16284
AAAI Technical Track on Computer Vision II