# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Amodal Appearance Reconstruction
#### 5.3.2 Face Completion  
Occlusion is usually present in faces. The occluding objects can be glasses, scarf, food, cup, microphone, etc. The performance of biometric and surveillance systems can degrade when faces are obstructed or covered by other objects, which raises a security concern. However, compared to background completion, facial images are more challenging to complete since they contain more appearance variations, especially around the eyes and the mouth. In the following, we categorize the available works for face completion based on their architecture. **A single generator and discriminator:** [PERSON] et al. [79] present an Occlusion-Aware GAN (OA-GAN), with a single generator and discriminator, that alleviates the need for an occlusion mask as an input. Through using paired images with known mask of artificial occlusions and natural images without occlusion masks, the model learns in a semi-supervised way. The generator has an occlusion-aware network and a face completion network. The first network estimates the mask for the area where the occlusion is present, which is fed into the second network. The latter then completes the missing region based on the mask. The discriminator employs an adversarial loss, and an attribute preserving loss to ensure that the generated facial image has similar attributes to the input image. Likewise, [PERSON] et al. [80] depend on their proposed OA-GAN to automatically identify the occluded region and inpaint it. They train a DCGAN on occlusion-free facial images, and use it to detect the corrupted regions. During the inpainting process, a binary matrix is maintained, which indicates the presence of occlusion in each pixel. The detection of occluded region alleviates the need for any prior knowledge of the location and type of the occlusion masks. However, incorrect occlusion detection leads to partially inpainted images. Facial Structure Guided GAN (FSG-GAN) [81] is a two-stage model with a single generator and discriminator. In the first part, a variational auto-encoder estimates the facial structure which is combined with the occluded image and fed into the generator of the second stage. The generator (UNet), guided by the facial structure knowledge, synthesizes the deoccluded image. A multi-receptive fields discriminator encourages a more natural and less ambiguous appearance of the output image. Nevertheless, the model cannot remove occlusion in a face image with large posture well, and it cannot correctly predict the facial structure under severe occlusions, which leads to unpleasant results. **Multiple discriminators:** Several of the existing works employ multiple discriminators to ensure that the completed facial image is semantically valid and consistent with the context of the image. [PERSON] et al. [82] train a model with a generator, a local discriminator, a global discriminator, and a parsing network to generate an occlusion-free facial image. The original image is masked with a randomly positioned noisy square and fed into the generator which is designed as an auto-encoder to fill the missing pixels. The discriminators, which are binary classifiers, enhance the semantic quality of the reconstructed pixels. Meanwhile, the parsing network enforces the harmony of the generated part and the present content. The model can handle various masks of different positions, sizes, and shapes. However, the limitations of the model include the facts that (1) it cannot recognize the position/orientation of the face and its corresponding elements which leadsto unpleasant generative content; (2) it fails to correctly recover the color of the lips; (3) it does not capture the full spatial correlations within neighboring pixels. Similarly, [PERSON] et al. [83] use an encoder-decoder for the generator, a Patch-GAN-based local discriminator, and a WGAN-GP [56]-based global discriminator to address occlusions on distinctive areas of a face and inpaint them. Consequently, the model's ability in recognizing faces improves.