  In this paper we investigate the vulnerability that facial recognition
systems present to adversarial examples by introducing a new methodology from
the attacker perspective. The technique is based on the use of the autoencoder
latent space, organized with principal component analysis. We intend to analyze
the potential to craft adversarial examples suitable for both dodging and
impersonation attacks, against state-of-the-art systems. Our initial
hypothesis, which was not strongly favoured by the results, stated that it
would be possible to separate between the "identity" and "facial expression"
features to produce high-quality examples. Despite the findings not supporting
it, the results sparked insights into adversarial examples generation and
opened new research avenues in the area.
