  Aiming to enhance Face Recognition (FR) on Low-Quality (LQ) inputs, recent
studies suggest incorporating synthetic LQ samples into training. Although
promising, the quality factors that are considered in these works are general
rather than FR-specific, \eg, atmospheric turbulence, resolution, \etc.
  Motivated by the observation of the vulnerability of current FR models to
even small Face Alignment Errors (FAE) in LQ images, we present a simple yet
effective method that considers FAE as another quality factor that is tailored
to FR. We seek to improve LQ FR by enhancing FR models' robustness to FAE. To
this aim, we formalize the problem as a combination of differentiable spatial
transformations and adversarial data augmentation in FR. We perturb the
alignment of the training samples using a controllable spatial transformation
and enrich the training with samples expressing FAE.
  We demonstrate the benefits of the proposed method by conducting evaluations
on IJB-B, IJB-C, IJB-S (+4.3\% Rank1), and TinyFace (+2.63\%).
\href{https://github.com/msed-Ebrahimi/ARoFace}{https://github.com/msed-Ebrahimi/ARoFace}
