## 3 Experiments
### The Proposed Thresholding Technique
Figure 10: The results of removing facial occlusion using the traditional depth-based value and using the proposed multi-feature combined threshold method. (**a**) The result of removing the covering hand, and (**b**) the result of removing the covering hair. To demonstrate the robustness of the proposed method, Figure 11 shows the results on the faces of different occlusion types after removing the occlusion using the depth-based removal of the occlusion and the proposed method. Figure 11a,b shows hand occlusion, while Figure 11c,d shows glasses occlusion and hair occlusion, respectively. the first column of Figure 11 is the multi-featured face under occlusion, the second column shows the depth-based removal results, the third column shows the removal results based on the proposed method, and the fourth column shows the difference between the two methods (that is, in Figure 11, the second column differs from the third column). The proposed method is robust to the detection of various types of occlusions, and it can remove more residual occlusions connected to the face than the depth-based method can; it can also reduce the impact of occlusion features on the facial recognition performance. Furthermore, we experimentally explore the impact of these two methods on the recognition performance. Table 3 shows the recognition of the obscured faces using raw training data and the missing face data generation method for training. When trained with the raw training data, the performance of the proposed thresholding technique decreases a little but is approximately equal in Top-1 recognition accuracy compared to the depth  
Figure 11: Results of removing hand, glasses, and hair occlusion using depth-based methods and the proposed method. From the left to right columns: multi-feature face under occlusion, depth-based removal results, removal results based on the proposed method, and differences between the two methods. (**a**) the mouth is covered by hand, (**b**) the right eye is covered by hand, (**c**) the eyes are covered by glasses, and (**d**) the face is covered by hair. based approach. However, its Top-5 recognition rate is higher by about 1.5%, indicating that the proposed thresholding technique has higher recognition potential. Moreover, compared with the obscured face recognition results produced using the same network structure parameters, as shown in Table 2, the accuracy of facial recognition after processing with both thresholding techniques decreases to some extent. The main reason for this phenomenon is the inadequacy of the raw training samples and the insufficient extraction of face differentiation features, thus making the impact caused by missing face data greater than the impact of occlusions. To solve this problem, we propose a sample generation method for missing face data to expand the training samples. Using this data generation method, the ability of the neural network to extract the key features of faces is greatly improved. As can be seen from the right area of Table 3, the proposed thresholding technique shows a significant improvement in both Top-1 and Top-5 recognition accuracy, with the Top-1 recognition rate increasing from 97.07% to 98.94% and the Top-5 recognition rate increasing from 98.67% to 99.47%. The proposed sample generation method has a significant enhancement effect. Moreover, when comparing it with the depth-based method, even though the samples are enhanced, the recognition rate of the depth-based method shows limited improvement. This indicates that the residual facial occlusion affects the intra-class aggregation of faces, so the removal of the residual occlusion can help to improve the recognition rate.