## 1 Introduction
They refine the local occlusion with morphological filtering and recover the whole face after removing the occluded part with Gappy PCA. [PERSON] [21] first finely aligns the nose region using a two-step alignment scheme and then identifies and removes the occluded region using a generic face model; finally, the whole face is recovered using Gappy PCA and the identity is confirmed using the score-level fusion of LDA classifiers. Similarly, [PERSON] [22] automatically discards the occluded objects using a thresholding method based on a comparison of the input depth image with the average face; this method then recovers the occluded part using PCA and extracts the normal face as recognition features. [PERSON] [23] adopts two modal methods for occlusion detection. He uses a Gaussian mixture model to compare the difference between the queried face and the model face to discriminate whether the surface pixels are valid values, treating the occlusion problem as a binary segmentation problem and obtaining the facial region using a spatial graph cut technique. [PERSON] [24] uses the connected region with the highest intensity value of the depth image acquired in Kinect as a potential occlusion region; this method then adjusts the boundary of the occlusion region and uses LBP to extract the distinguishing features. SVM is used for facial classification recognition. [PERSON] proposes a block-based approach based on the phenomenon whereby salient parts have higher depth densities over the whole surface. He progressively computes the depth map depth values of two blocks of different sizes, scrolling along the row direction on the depth map to identify the occlusion targets. [PERSON] et al. [25] use the Gappy wavelet neural network for occlusion rejection. They compare the wavelet coefficients of probe 3D faces with the wavelet coefficients of average 3 Dfaces, thus detecting and removing the occluded objects. [PERSON] et al. [26] propose a region-classifier-based recognition strategy. They detect and remove occlusions using fuzzy C-mean clustering and the shape index (SI) and then represent the whole face depth with LBP and divide it into three horizontal regions (eyes, nose, and mouth). Then, they create facial recognition features using HOG descriptors. [PERSON] et al. [27] achieve the double detection of occlusion by detecting the overall difference rate between the left and right sides of the face and comparing the intensity difference between the query face and the face model; they then gradually remove the occluded region and use 12 differential geometric descriptors to recognize the face. Researchers have also proposed other methods for 3D occlusion facial recognition. [PERSON] [28] proposes a 3D statistical facial feature model. The model is used to learn the variations in global configuration relations of 3D facial landmarks and the local variations in the texture and geometric aspects of each landmark. Finally, a k-nearest-neighbor classifier is used to recognize obscured faces. [PERSON] et al. [29] use the method of directly detecting three nose regions (the whole nose and left and right nasal flaps) based on template matching. Even if there are facial occlusion regions, nose recognition can be accomplished by matching the average nose model and the facial depth image. [PERSON] et al. [30] improve the ICP algorithm for occlusion facial recognition. The geometric surface is represented as a spherical depth map for fast and uniform sampling. Then, a rejection strategy is embedded in ICP to eliminate the occluded objects. When a facial occlusion object is detected, it is usually processed by removing the occluded part or restoring the occluded area. However, restoring the occluded area decreases recognition rates, and using only non-obscured facial surfaces is more beneficial for facial recognition [31; 32]. Meanwhile, rapidly developing deep learning techniques are receiving more attention in the field of facial recognition [10; 33; 34; 35].