## 3 Experiments
### The Proposed Thresholding Technique  
To illustrate the accuracy of the proposed multi-feature combinatorial thresholding method for occlusion localization, we compare it with the traditional methods based on directly localizing occlusion regions based on depth information [21; 24; 57; 58]. After locating the occlusion, we directly remove the occlusion. Figure 10 shows the results of removing the hand occlusion and hair occlusion using the proposed method and the traditional method for the two occlusion cases. The results of removing the hand occlusion using the two methods are shown in Figure 10a region I. The enlarged view of the local \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Face Representation**} & \multirow{2}{*}{**Size/Loss**} & \multicolumn{2}{c}{**Posture and Expression**} & \multicolumn{2}{c}{**Occlusion**} & \multicolumn{2}{c}{**Average**} \\ \cline{3-7}  & & **Top-1** & **Top-5** & **Top-1** & **Top-5** & **Top-1** & **Top-5** \\ \hline
[RGB] & 112\(\times\)112/cross-entropy & 94.89 & 97.11 & 97.09 & 98.41 & 95.89 & 97.71 \\
[D] & 112\(\times\)112/cross-entropy & 96.00 & 97.56 & 91.27 & 97.35 & 93.84 & 97.22 \\
[D, SL C] & 112\(\times\)112/cross-entropy & 95.33 & 97.11 & 94.71 & 98.41 & 95.05 & 97.71 \\
[D, A, E, SI, C] & 112\(\times\)112/cross-entropy & 96.00 & 97.33 & 94.97 & 97.62 & 95.53 & 97.46 \\
[D, A, E] & 112\(\times\)112/cross-entropy & 97.11 & 98.89 & 96.56 & 99.47 & 96.86 & 99.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recognition accuracy (%) for the Bosphorus dataset for different facial representation selections. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Loss**} & \multicolumn{2}{c}{**Posture and Expression**} & \multicolumn{2}{c}{**Occlusion**} & \multicolumn{2}{c}{**Average**} \\ \cline{3-8}  & & **Top-1** & **Top-5** & **Top-1** & **Top-5** & **Top-1** & **Top-5** \\ \hline
224\(\times\)224 & Focal-Arcface & 98.89 & 99.56 & 94.97 & 98.68 & 97.10 & 99.15 \\
160\(\times\)160 & Focal-Arcface & 98.00 & 99.33 & 93.39 & 98.41 & 95.89 & 98.91 \\
96\(\times\)96 & Focal-Arcface & 98.89 & 99.33 & 96.03 & 98.68 & 97.58 & 99.03 \\ \hline
112\(\times\)112 & Cross-entropy & 97.11 & 98.89 & 96.56 & 99.47 & 96.86 & 99.15 \\
112\(\times\)112 & Focal & 98.67 & 99.33 & 92.06 & 97.88 & 95.65 & 98.67 \\ \hline
112\(\times\)112 & Focal-Arcface & 99.33 & 99.78 & 98.15 & 99.47 & 98.79 & 99.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recognition accuracy (%) for the Bosphorus dataset under different input dimension and loss parameter settings. Figure 9: Different face representations: (**a**) RGB; (**b**) elevation of surface normal defined as E; (**c**) azimuth of surface normal defined as A; (**d**) depth map defined as D; (**e**) shape index; (**f**) curvedness. area (red box area) shows that the proposed method can remove more occluded areas. To understand the difference between the two removal methods more intuitively, we highlight the difference between the two de-obscured faces; see region II in Figure 10a. It can be seen from region II in Figure 10a that the depth-based method tends to miss the occlusions close to the face's surface, while the proposed method can reduce the residual occlusions. To fully illustrate the accuracy of the proposed method in locating the occluded region, we compared the hair occlusion case in the same way as shown in Figure 10b. The depth-based method in Figure 10b region III is unable to remove the hair that sits closely to the face, while the proposed method can remove most of the hair, as shown in the red boxed region in Figure 10b, region III. Similarly, Figure 10b region IV is the result of showing the difference. Obviously, the proposed method removes more of the occlusion regions that cannot be removed by depth-based methods. The reason for this is that the proposed method not only considers the facial depth information but also introduces the normal information, which is more sensitive to surface variation.