# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 2 Related Work
### Ofer  
In the real world, human facial regions are readily occluded by external factors such as sunglasses, hats, scarves, and masks. Occlusions are divided into two types, based on whether an object is in the original image or not. The first type is artificial occlusions, where objects are added to the image to mimic occlusions. The second type is real occlusions, which are already present in the original image. These occlusions can harm FER accuracy by increasing intra-class differences or inter-class similarity. There are numerous studies on the OFER problem, which can be divided into four principal categories: robust feature extraction under occlusion, sub-region analysis, non-occluded network image assistance, and occlusion recovery methods.  
(1) Robust feature extraction under occlusion The objective of this method is to extract features that are less affected by occlusion while maintaining the capacity to discriminate between different categories. [PERSON] et al. [9] demonstrated how the introduction of artificial occlusion affects the performance of the FER system and discussed methods to address this issue. [PERSON] et al. [15] proposed a region attention network (RAN) to capture crucial regions of the face, thereby enabling the solution of FER tasks with occlusion and pose changes. They also introduced RB-Loss to improve the importance of region attention weights.  
(2) Sub-region analysis This method is designed to exclude the occluded regions from the recognition process. The impact of occlusion on the recognition performance of the FER model is reduced by focusing only on the unoccluded facial regions. [PERSON] et al. [30] proposed a new gated convolutional neural network (PG-CNN) that focuses on different regions of the face image and automatically detects the occluded regions of the face so that the network can focus on the more informative unoccluded regions. [PERSON] et al. [14] designed a block-based attention network for OFER. This network inputs a selection of 24 blocks, cropped from the regions of the eyes, nose, mouth, etc., into an attention network to compute the corresponding weights. These weights are then fused with the global features, allowing the model to focus on the information-rich unoccluded facial regions.  
(3) Non-occluded network image assistance The method employs unoccluded facial images as a reference to facilitate the operation of OFER. [PERSON] et al. [16] addressed the OFER challenge by training two deep neural networks, with one network focusing on processing unoccluded facial images and the other specializing in processing occluded facial images. The unoccluded network provides guidance to the occluded network, facilitating the more precise recognition of facial features in the occluded region. The fundamental principle of this approach is to utilize the comprehensive data present in the unoccluded facial image to facilitate enhanced recognition in the occluded image. [PERSON] et al. [17] employed distribution densities in the feature space to quantify the complexity of unoccluded data. The unoccluded data can inform the classifier, enabling the identification and utilization of more meaningful and discriminative samples. By relying on the full information provided by the unoccluded data, the classifier is able to better understand and distinguish between different classes of features, improving the overall recognition performance.  
(4) Occlusion recovery methods The objective of this method is to recover the occluded region of the image. [PERSON] [31] proposed a novel FER method based on Gabor multi-directional feature fusion and local Gabor binary pattern histogram sequence (LGBPHS) techniques to address the issue of a partially occluded face image. In order to reconstruct the occluded face, [PERSON] et al. [32] proposed a Wasserstein generative adversarial network-based method, which consists of an autoencoder-based generator and a discriminator. This approach allows for the reconstruction of the occlusion, thereby obtaining an information-rich appearance.