Reference Selection for Offline Hybrid Siamese Signature Verification Systems

Abstract

This paper presents an off-line handwritten signature verification system based on the Siamese network, where a hybrid architecture is used. The Residual neural Network (ResNet) is used to realize a powerful feature extraction model such that Writer Independent (WI) features can be effectively learned. A single-layer Siamese Neural Network (NN) is used to realize a Writer Dependent (WD) classifier such that the storage space can be minimized. For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively, we propose a method of selecting a reference signature as one of the inputs for the Siamese network. To take full advantage of the reference signature, we modify the conventional contrastive loss function to enhance the accuracy. By using the proposed techniques, the accuracy of the system can be increased by 5.9%. Based on the GPDS signature dataset, the proposed system is able to achieve an accuracy of 94.61% which is better than the accuracy achieved by the current state-of-the-art work.