On Recognizing Occluded Faces in the Wild
This addresses the challenge of occluded face recognition for computer vision researchers, though it is incremental as it focuses on dataset creation.
The authors tackled the problem of face recognition under real-world occlusion by introducing the Real World Occluded Faces (ROF) dataset, showing that deep face models' performance degrades significantly on real occlusions compared to synthetic ones.
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.