## 1 Introduction
Deep learning methods extract the deep features of facial data through large-scale training sets and different neural network structures and use specific loss functions to achieve the inter-class separation and intra-class aggregation of facial features. [PERSON] et al. [36] show in an experiment that combining different texture features and depth features for deep learning is more effective than considering only a single facial feature. In this paper, we propose a 3D occlusion facial recognition network based on a multi-feature combination threshold, hereafter denoted as MFCT-3 DOFRNet for convenience. First, we use the least squares method to solve the optimal transformation matrix from the input 3D face to the reference face and use this transformation matrix to implement the pose correction of the face to be recognized. Then, to make better use of the existing depth recognition network structure, we convert the face 3D point cloud into a depth image. Additionally, we extract the angular information of the point cloud normal vectors as features for facial geometry differentiation. We use MobileNet, a lightweight feature extraction network, as the backbone network, and introduce the Focal-ArcFace loss function in the training of the network parameters to improve the network model's ability to extract the implicit features of human faces. For the redundant occlusion information in the feature maps, we use a multi-feature combinatorial thresholding technique to remove the regions of excessive differences between the detected face and the mixed average face model (mixed AFM). We also introduce missing face data generation methods to expand the training samples and improve the network's recognition performance for de-obscured faces. The experimental results show that the performance of 3D occlusion facial recognition is effectively improved, since most of the interference information is removed before the network feature extraction. In general, the main contributions of the proposed algorithm are as follows:  
* We propose a 3D occlusion facial recognition network. We represent the face point cloud as different geometric feature maps for recognition and de-occlusion tasks. We use a lightweight network as the backbone network to reduce the size of the model parameters and use Focal-Arcface loss to enhance the intra-class aggregation of the recognition network for missing face data. * We propose a method for removing facial occlusion from 3D faces based on a multi-feature combinatorial thresholding method. Compared with relying only on depth information to determine the occlusion areas, the multi-feature thresholding technique can remove the occlusion with obvious depth distance from the face and can better locate the boundary between the face and occlusion. This method does not require changing the original structure of the model since it only needs to embed the input side of the neural network. * A mixed average face model (mixed AFM) construction method is proposed. We form a new facial representation after characterizing the 3D face point cloud as a collection of facial features with different feature attributes; then, we construct the average face and standard deviation of the respective feature channels point by point (this is conducted offline). * We propose a missing facial data generation method for convolutional network training. Compared to the original dataset, the proposed method expands the amount of data for each face by 23 times. The model parameters trained using this dataset improve the recognition rate for faces with expression changes, pose changes, and the removal of occlusions. The remainder of this paper is organized as follows. Section 2 describes the basic theory of the proposed method. Section 3 presents the experimental results. The conclusions are discussed in Section 4.