# A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network
## 5 Results and Discussion
### Network Performance Analysis  
This paper proposes a joint optimization method of Regular Face loss and Softmax loss, which integrates the expression classification task based on Softmax loss and the metric learning task based on Regular Face loss into the proposed network. Softmax loss can make the model learn expression features, and the Regular Face loss can ensure that the expression features are learned by the model. Through multi-task learning, the advantages of the two tasks are brought into play, so as to improve the discrimination capacity and robustness of the face recognition. In order to verify the performance of the Regular Face loss function, this paper compares the recognition accuracy before and after repair when the Regular Face + SoftMax (abbreviated as R + S) loss function is used and only the SoftMax (abbreviated as S) loss function is used [31]. The experimental results are shown in Figure 9.  
Figure 9: _Cont._  
It can be seen from Figure 9A that the face before repair has lost too many features. No matter which loss function is used, the recognition accuracy is very low, but the loss function of Regular Face + SoftMax is still partly improved compared to SoftMax. It can be seen from Figure 9B that the recognition accuracy after the repair has been significantly improved, and the improvement effect of using the Regular Face + SoftMax loss function is also more obvious, especially the occlusion of facial features. This is because Regular Face has the characteristic of quantifying the distance between classes. The effect of these difficult-to-classify features has been improved more obviously.  
For face images with different occlusion areas, the filled face images are input into the face recognition network, and the faces are discriminated. The experimental results are shown in Figure 9C. It can be seen that the face filling model improves the accuracy of occluded expression recognition as a whole. When the occlusion area is less than 50%, the overall recognition rate of the model is still above 80%, which is close to 20% higher than the direct use of convolutional neural networks. When the occlusion area is small, the recognition accuracy of the model has a small improvement. Considering the processing time of the model, system resources and other factors, when the occlusion area is less than 10% of the face area, no filling is required, and the convolutional neural network is directly used for simple classification and recognition [32].  
In this paper, the PCA + SVM method, sparse representation-based classification (SRC) and CNN, DCGAN + CNN methods are selected for comparison. In the DCGAN + CNN method, DCGAN is used to fill the occluded face image, and the CNN is a fine-tuned VGG model. All methods used for facial expression classification were tested on the CK + data set, and the results are shown in Table 3. It can be seen from the table that regardless of whether the face image is occluded, the facial expression recognition rate of the method in this paper is high. Although the method based on DCGAN + CNN also fills in the face image, the coherence of the image after the repair is poor, which affects the accuracy of expression recognition [33], especially the occlusion area that is less than 40%. The accuracy of facial expression recognition of this method is even lower than that of CNN alone. The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. Therefore, when the occlusion area of the face is 50%, a recognition accuracy rate of more than 80% can still be achieved.  
Figure 9: Comparison of recognition accuracy after different levels of occlusion restoration.