Static-Dynamic Interaction Networks for Offline Signature Verification

Authors

  • Huan Li Xi’an Jiaotong University
  • Ping Wei Xi'an Jiaotong University
  • Ping Hu Xi'an Jiaotong University

Keywords:

Biometrics, Face, Gesture & Pose, Biometrics, (Deep) Neural Network Algorithms, Security

Abstract

Offline 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.

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Published

2021-05-18

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

Issue

Section

AAAI Technical Track on Computer Vision II