Inverse Discriminative Networks for Handwritten Signature Verification

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.