  Facial appearance variations due to occlusion has been one of the main
challenges for face recognition systems. To facilitate further research in this
area, it is necessary and important to have occluded face datasets collected
from real-world, as synthetically generated occluded faces cannot represent the
nature of the problem. In this paper, we present the Real World Occluded Faces
(ROF) dataset, that contains faces with both upper face occlusion, due to
sunglasses, and lower face occlusion, due to masks. We propose two evaluation
protocols for this dataset. Benchmark experiments on the dataset have shown
that no matter how powerful the deep face representation models are, their
performance degrades significantly when they are tested on real-world occluded
faces. It is observed that the performance drop is far less when the models are
tested on synthetically generated occluded faces. The ROF dataset and the
associated evaluation protocols are publicly available at the following link
https://github.com/ekremerakin/RealWorldOccludedFaces.
