On Usage of Autoencoders and Siamese Networks for Online Handwritten Signature Verification

• Published in: arXiv.org
• Main Authors: Ahrabian, Kian, Babaali, Bagher
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 29.12.2017
• Subjects: Accuracy; Artificial neural networks; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Neural and Evolutionary Computing; Feature extraction; Handwriting; Handwritten signature verification
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1712.02781

In this paper, we propose a novel writer-independent global feature extraction framework for the task of automatic signature verification which aims to make robust systems for automatically distinguishing negative and positive samples. Our method consists of an autoencoder for modeling the sample space into a fixed length latent space and a Siamese Network for classifying the fixed-length samples obtained from the autoencoder based on the reference samples of a subject as being "Genuine" or "Forged." During our experiments, usage of Attention Mechanism and applying Downsampling significantly improved the accuracy of the proposed framework. We evaluated our proposed framework using SigWiComp2013 Japanese and GPDSsyntheticOnLineOffLineSignature datasets. On the SigWiComp2013 Japanese dataset, we achieved 8.65% EER that means 1.2% relative improvement compared to the best-reported result. Furthermore, on the GPDSsyntheticOnLineOffLineSignature dataset, we achieved average EERs of 0.13%, 0.12%, 0.21% and 0.25% respectively for 150, 300, 1000 and 2000 test subjects which indicates improvement of relative EER on the best-reported result by 95.67%, 95.26%, 92.9% and 91.52% respectively. Apart from the accuracy gain, because of the nature of our proposed framework which is based on neural networks and consequently is as simple as some consecutive matrix multiplications, it has less computational cost than conventional methods such as DTW and could be used concurrently on devices such as GPU, TPU, etc.