# MCF-Net: Fusion Network of Facial and Scene Features for Expression Recognition in the Wild
## 4 Results
### Experiments and Result Analysis  
#### 4.2.1 Experiments and Result Analysis of MCF-Net on CAER-S Dataset  
In order to verify the effectiveness of MCF-Net, our model is tested and validated on the CAER-S dataset. The accuracy curve of MCF-Net is shown in Figure 7, in which the red line indicates the training accuracy, and the blue line represents the validation accuracy. It can be seen from Figure 7 that both training accuracy and verification accuracy of MCF-Net gradually improved. The accuracy begins to converge around the epoch of 30 and tends to be stable around the epoch of 45, which shows that the proposed MCF-Net is reasonable and effective. Figure 5: Sample frames of the CAER-S dataset. Figure 6: Percentage of each emotion category in CAER and NCAER-S training sets. The loss function curve of the MCF-Net obtained during the training process is shown in Figure 8, where the red line represents the training loss, and the blue line means the validation loss. It can be concluded that both training loss and validation loss of our model gradually decrease and begin to converge around the epoch of 30. At the epoch of 45, the curve tends to be stable, and the loss value no longer changes, finally stabilizing at 0.71. The results prove that MCF-Net can better fit the distribution of samples in the training set and the validation set, which verify the rationality of the joint loss at the same time. Our model is compared with some classical and SOTA methods on the CAER-S dataset, as shown in Table 1. It can be seen that the accuracy of MCF-Net is higher than that of baseline CAER-Net-S by 2.17% and much higher than those of classical methods, with only a small increase in parameters. In order to improve the performance of recognition, ResNet-18 is adopted to replace the shallow convolution and pooling layers of the face coding stream. When compared with other recent state-of-the-art methods, we obtained state-of-the-art performance on the CAER-S dataset with 81.82% classification accuracy. Although the parameters of MCF-Net(ResNet18) have been increased, it achieves a great improvement in recognition accuracy. These results confirm that adding three modules can effectively encode both facial information and context information to improve the facial expression classification results. Furthermore, the parameters prove that the training time consumption of our model is short, and the computational cost is less. To specify, the calculation cost is reported in Multiply-Accumulates (MAC), averaged over all test images. From Table 1, it can be seen that the calculation costs of our MCF-Net and the  
Figure 8: The loss curve of MCF-Net on CAER-S dataset. Figure 7: The accuracy curve of MCF-Net on CAER-S dataset. original CAER-Net-S are 369 MMAC and 385 MMAC, respectively. Besides, the calculation cost decreases as the accuracy of our model improved. In order to prove the recognition effect of the improved model on each category of samples, the confusion matrix of CAER-Net-S and MCF-Net (ResNet18) on CAER-S datasets is shown in Figures 9 and 10, respectively. In the confusion matrix, the horizontal represents the predicted label while the vertical indicates the real label. The values of the diagonal show the classification accuracy of the network for each category, and darker colors represent higher accuracy.