  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.
