  As advances in signature recognition have reached a new plateau of
performance at around 2% error rate, it is interesting to investigate
alternative approaches. The approach detailed in this paper looks at using
Variational Auto-Encoders (VAEs) to learn a latent space representation of
genuine signatures. This is then used to pass unlabelled signatures such that
only the genuine ones will successfully be reconstructed by the VAE. This
latent space representation and the reconstruction loss is subsequently used by
random forest and kNN classifiers for prediction. Subsequently, VAE
disentanglement and the possibility of posterior collapse are ascertained and
analysed. The final results suggest that while this method performs less well
than existing alternatives, further work may allow this to be used as part of
an ensemble for future models.
