  Contemporary face hallucination (FH) models exhibit considerable ability to
reconstruct high-resolution (HR) details from low-resolution (LR) face images.
This ability is commonly learned from examples of corresponding HR-LR image
pairs, created by artificially down-sampling the HR ground truth data. This
down-sampling (or degradation) procedure not only defines the characteristics
of the LR training data, but also determines the type of image degradations the
learned FH models are eventually able to handle. If the image characteristics
encountered with real-world LR images differ from the ones seen during
training, FH models are still expected to perform well, but in practice may not
produce the desired results. In this paper we study this problem and explore
the bias introduced into FH models by the characteristics of the training data.
We systematically analyze the generalization capabilities of several FH models
in various scenarios, where the image the degradation function does not match
the training setup and conduct experiments with synthetically downgraded as
well as real-life low-quality images. We make several interesting findings that
provide insight into existing problems with FH models and point to future
research directions.
