Research on offline signature verification based on image domain transfer Siamese network
Wanli Liao, Zhicong Lin, Zhuohui Chen, Guanru Zou, Ling Chen · 2023
Verifying individual Chinese handwritten signatures is an essential biometric technology that is widely used in banking, finance, and legal business. The forging of signatures for the purpose of cheating is a serious detriment to the interests of these industries. This paper proposes a Siamese network verification signature based on image domain transfer. The Siamese network uses a convolutional neural network as a sub-network to build the structure of Siamese network by combining the genuine signature network and the unauthenticated signature network. Each sub-network transfers ImageNet weights for training in the Chinese recognition task so that the new weighted image domain is suitable for the Chinese signature image domain. This Siamese network is trained with new weights to determine the authenticity of the signature. Currently, there is no publicly available dataset of Chinese handwritten signatures. This paper develops the Chinese signatures dataset from 295 induvial persons, 885 different persons participated, including approximately 9,000 pictures. The Siamese network achieves 92.75% accuracy on the test set, and the verification time for a single Chinese handwritten signature is 0.32 seconds. Finally, the Siamese network model achieves more than 90% accuracy on the public handwritten signature datasets CEDAR, BHSig-B and BHSig-H in three different languages, and the experiments demonstrate the good generalization of the proposed method.