## 4 Conclusions  
In this paper, we propose a 3D occlusion face recognition network based on a multi-feature combination threshold (MFCT-3 DOFFNet) to solve the problem of robust facial recognition when 3D faces are occluded in unconstrained environments. This network transforms the 3D face point cloud into a multi-featured face through a new form of facial representation that can preserve the geometric information of the face and, at the same time, draw on existing lightweight deep network techniques. To prevent occlusions from blending with a face's distinguishing features, we propose a multi-feature combination threshold to remove occluded regions from faces, which can increase the inter-class separation and intra-class aggregation capabilities of the depth features. We also propose using a missing face data generation method when the network cannot fully utilize the distinguishing features of faces after de-obscuring a face due to insufficient training samples using missing face data. The method simulates the situation of face data being lost after various de-occlusions, which makes the neural network training more focused on features that can more easily differentiate faces and also reduces the intra-class separation of features caused by missing data. The experimental results show that the proposed facial recognition method can effectively improve the final recognition performance and has strong robustness.  
Without prior knowledge of occlusion, the proposed method can automatically remove the occluded facial area and achieves an average Top-1 recognition rate of 99.52% and 95.08% on Bosphorus and UMB-DB, respectively. However, one limitation of our work is that the accuracy of facial occlusion localization is affected by the 3D face alignment performance. In future work, we will consider improving the localization performance of occlusion face alignment [66] and explore new network architectures [67; 68] to improve the recognition accuracy of occluded faces.  
Conceptualization, K.Z.; methodology, K.Z.; software, K.Z.; validation, K.Z., X.H. ([PERSON]) and K.Z.; formal analysis, R.H. and J.W.; investigation, Z.L.; resources, Z.M. and X.H. ([PERSON]); data curation, X.Z.; writing--original draft preparation, K.Z., Z.M. and X.H. ([PERSON]); writing--review and editing, X.H. ([PERSON]); visualization, J.H.; supervision, L.Z. and J.H.; project administration, Z.L.; funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript. This work was supported by the Opening Project of Key Laboratory of Sichuan Universities of Criminal Examination (2018 YB03).  
Not applicable.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MFCT-3 DOFFNet \(u/o\)** & **MFCT-3 DOFFNet \(u/o\)** & **MFCT-3 DOFFNet \(u/o\)** & **MFCT-3 DOFFNet \(u/o\)** \\  & **Multi-Feature Combined** & **Missing Face** & **Focal-Arcface** & **MFCT-3 DOFFNet** \\  & **Threshold Method** & **Data Generation** & **Focal-Arcface** & \\ \hline Avg. & 94.62 & 87.99 & 92.44 & 95.08 \\ Enhance Percent & 0.49\% & 8.06\% & 2.9\% & / \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments on the UMB-DB database. This table records the average recognition rate of occluded and non-occluded faces. The enhance percentage is the influence of the module on the overall performance.  
Data Availability Statement: Bosphorus database ([[http://bosphorus.ee.boun.edu.tr/default.aspx](http://bosphorus.ee.boun.edu.tr/default.aspx)]([http://bosphorus.ee.boun.edu.tr/default.aspx](http://bosphorus.ee.boun.edu.tr/default.aspx)) (accessed on 28 June 2022)). UMB-DB database ([[http://www.ivl.disco.unimib.it/minisites/umbdb/](http://www.ivl.disco.unimib.it/minisites/umbdb/)]([http://www.ivl.disco.unimib.it/minisites/umbdb/](http://www.ivl.disco.unimib.it/minisites/umbdb/)) (accessed on 18 July 2022)).  
Conflicts of Interest: The authors declare no conflict of interest.