## 1 Introduction  
In recent years, two-dimensional facial recognition technologies that use 2D grayscale or color image information have been widely used in identity identification, security monitoring, and other fields. However, the measured face is often unconstrained in real recognition scenarios, and 2D faces are easily affected by ambient light, the shooting posture, facial makeup, and other factors that reduce the technology's recognition performance [1]. Studies show that 3D imaging techniques such as structured light and TOF are insensitive to 3D imaging of the face with lighting and makeup changes [2, 3], and multi-pose 3D faces can also be corrected by different alignment algorithms [4, 5, 6]. Therefore, with the rapid development of 3D imaging devices, the robust depth information and facial geometry information contained in 3D face data support a comprehensive understanding of facial features, overcoming the basic limitations of 2D faces in terms of lighting changes, pose changes, makeup, etc. [7, 8]. However, in extreme unconstrained recognition scenarios, faces are often obscured by random external components (glasses, scarves, palms, etc.). Knowing the location and morphology of the occluded object in advance is not feasible; therefore, facial geometric features changes significantly when occlusion occurs, and the contaminated facial recognition information affects the accuracy of the final recognition algorithm. The higher inter-class similarity and greater intra-class variation caused by occlusion can impair the recognition accuracy for the face to be recognized [9]. Large-scale 3D databases are not widely available, whereas several publicly available large-scale 2D face databases already exist [10]. From the feasibility point of view, it is difficult to directly obtain a 3D database that accounts for all facial occlusion possibilities and uses deep learning techniques for facial recognition [9; 11]. Therefore, 3D occlusion facial recognition constitutes a much more common and difficult problem. Compared with the 3D recognition of pose and expression changes, there are fewer related studies on the occlusion problem [12; 13; 14]. However, as occlusion recognition in unconstrained environments has received more and more attention from researchers, some strategies have been proposed. The strategies for 3D occlusion facial recognition mainly comprise, on the one hand, methods based on facial curves and, on the other, those using non-occluded facial regions. [PERSON] et al. [15] use radial curves to represent 3D faces. They use ICP to detect and remove the external occlusion points on each curve and retain the high-quality curves using a quality filter; after that, they use the statistical model in the curve shape's space to fill in the missing data for the whole area to achieve facial recognition. [PERSON] et al. [16] use indexed sets using radial geodesic curves to represent 3D human faces. They compare facial curve shapes using elastic shape analysis and process the occluded parts using recursive-ICP. [PERSON] et al. [17; 18] propose a new radial string representation and matching algorithm. They represent 3D faces with an indexed collection of attributed strings in the radial curve direction. The occlusion is removed by obtaining the similarity of the corresponding radial strings using a dynamic programming method, and faces are recognized using the most discriminative parts. [PERSON] et al. [19] use central profile curves in the nasal tip region to form a rejection classifier to quickly filter dissimilar faces. They segment the facial region that is most sensitive to occlusions into six blocks and extract the facial deformation curves of the corresponding blocks, and then discard the occluded regions using an adaptive region selection strategy to achieve the accurate recognition of faces. [PERSON] et al. [20] detect the occluded region by comparing the probe face with a generic model of the face obtained using the facial feature method.