  To recognize the masked face, one of the possible solutions could be to
restore the occluded part of the face first and then apply the face recognition
method. Inspired by the recent image inpainting methods, we propose an
end-to-end hybrid masked face recognition system, namely HiMFR, consisting of
three significant parts: masked face detector, face inpainting, and face
recognition. The masked face detector module applies a pretrained Vision
Transformer (ViT\_b32) to detect whether faces are covered with masked or not.
The inpainting module uses a fine-tune image inpainting model based on a
Generative Adversarial Network (GAN) to restore faces. Finally, the hybrid face
recognition module based on ViT with an EfficientNetB3 backbone recognizes the
faces. We have implemented and evaluated our proposed method on four different
publicly available datasets: CelebA, SSDMNV2, MAFA, {Pubfig83} with our locally
collected small dataset, namely Face5. Comprehensive experimental results show
the efficacy of the proposed HiMFR method with competitive performance. Code is
available at https://github.com/mdhosen/HiMFR
