## 3 Experiments
### Model Analysis  
We identify the impact of the settings of different module parameters, such as facial representation, the network input size, and model training, on the proposed 3D face recognition task to achieve the optimal operation of the proposed obscured face recognition framework. We use the face data from the Bosphorus database with pre-processing and multi-feature characterization as input data for our network model analysis; we divide the data into a training set, a validation set, and a test set with the ratio of 8:1:1. By default, we only change the structure of the part to be analyzed, and the other network structures remain the same. We verify the reasonableness of the set parameters based on the results of the analysis and gradually act on the subsequent experiments.  
Figure 9 shows the different face representations. From left to right are the RGB map, the elevation and azimuth angles of the surface normal, the depth map, the shape index, and the curvedness. We train the neural network parameters with different combinations of these facial representations, and then compare the recognition performance when the facial representations are different. The experimental results of constructing a suitable data composite representation of the face are shown in Table 1. The network structure of this experiment is uniformly adjusted to 112 \(\times\) 112 for the input image, the backbone network structure is set to MobileNet, and the loss function is set to the cross-entropy loss function. As seen in Table 1, the construction of the new 3D face representation using depth information and normal azimuth and elevation information can lead to a better recognition rate. Better Top-1 and Top-5 facial recognition accuracy is achieved in almost all cases of pose, expression, and occlusion. It is worth noting that the recognition accuracy of the RGB faces is higher for the occluded faces of Top-1, since the occlusion of the Bosphorus database mainly comprises occlusion with the subject's own arm, with similar RGB skin tones. Additionally, the experimental results show the same experimental conclusions as [PERSON] [56]. That is, having more face representations is not always optimal, and the ability of different pieces of representational information to complement each other helps to improve the recognition rate.  
Table 2 shows the effect of different input image dimensions and setting different loss functions on the recognition accuracy of the network model. The backbone network of this experiment is kept as MobileNet; we fix the loss as Focal-Arcface and change the input image dimensions to 96 \(\times\) 96, 112 \(\times\) 112, 160 \(\times\) 160, and 224 \(\times\) 224. The posture and expression accuracy, occlusion accuracy, and average accuracy do not simply vary linearly with the input size. The experiments show that the output size has the best Top recognition rate when it is set to 112 \(\times\) 112. Similarly, we fix the input image dimension to 112 \(\times\) 112 and set the loss functions for training as cross-entropy, Focal and Focal-Arcface. Using Focal-Arcface as the loss function for training helps to improve our Top-1 and Top-5 recognition accuracy. Therefore, our proposed MFCT-3 DOFRNet recognition network keeps the input 3D facial representation as a multi-featured face with a combination of depth information and azimuthal and elevation information of the normal. The input dimension is 112 \(\times\) 112 and the loss function for training is Focal-Arcface.