Inverse Discriminative Networks for Handwritten Signature Verification

Ping Wei, Huan Li, Ping Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5764-5772

Abstract


Handwritten signature verification is an important technique for many financial, commercial, and forensic applications. In this paper, we propose an inverse discriminative network (IDN) for writer-independent handwritten signature verification, which aims to determine whether a test signature is genuine or forged compared to the reference signature. The IDN model contains four weight-shared neural network streams, of which two receiving the original signature images are the discriminative streams and the other two addressing the gray-inverted images form the inverse streams. Multiple paths of attention modules connect the discriminative streams and the inverse streams to propagate messages. With the inverse streams and the multi-path attention modules, the IDN model intensifies the effective information of signature verification. Since there was no proper Chinese signature dataset in the community, we collected a large-scale Chinese signature dataset with approximately 29,000 images of 749 individuals' signatures. We test our method on the Chinese signature dataset and other three signature datasets of different languages: CEDAR, BHSig-B, and BHSig-H. Experiments prove the strength and potential of our method.

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[bibtex]
@InProceedings{Wei_2019_CVPR,
author = {Wei, Ping and Li, Huan and Hu, Ping},
title = {Inverse Discriminative Networks for Handwritten Signature Verification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}