Occlusion-Aware GAN for Face De-Occlusion in the Wild

Occluded faces---as a common scene in real life---have a significant negative impact on most face recognition systems. Existing methods try to remove the occlusions by a single-stage generative adversarial network (GAN), which is unaware of the occlusion and thus has difficulties in generalizing to a large variety of occlusion types. To this end, we propose two-stage Occlusion-Aware GAN (OA-GAN), where the first GAN is for disentangling the occlusions, which will be served as the additional input of the second GAN for synthesizing the final de-occluded faces. In this way, our two-stage model can handle diverse occlusions in the wild and is naturally more explainable because of its awareness of the occluded objects. Extensive experiments on both synthetic and real-world datasets validate the superiority of the two-stage OA-GAN design. Furthermore, by applying the generated de-occluded faces to facial expression recognition (FER) systems, we find that our two-stage de-occlusion process significantly increases the accuracy of FER under occlusion.