# EC-GAN: Emotion-Controllable GAN for Face Image Completion
## 4 Experiments
### Ablation Study  
In order to study the contributions of our emotion inference module, we conducted an ablation study on the CFEED dataset [27]. EC-GAN models are trained and tested with and without the emotion inference module for the center-missing case, aiming to complete an image with diverse emotions. We named them model I (with the emotion inference module, it is also the EC-GAN model) and model II (without the emotion inference module), respectively. Figure 9 shows some completion results.  
It can be observed that Model I (EC-GAN) is capable of customizing the same emotion semantics as the ground truths. Meanwhile, it also customizes different expressions while preserving other facial semantics successfully. On the other hand, Model II can also generate the correct facial expressions, but other important facial features are altered in the meantime. As shown in Figure 9, the male images appear to have feminine attributes after the completion. Additionally, the face tones of some images also change significantly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & FID \(\downarrow\) & IS \(\uparrow\) & SSIM \(\uparrow\) & PSNR \(\uparrow\) & MAE \(\downarrow\) \\ \hline EC-GAN & 5.47 & 24.58 & 0.93 & 28.01 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative results for random masks on CFEED [27].  
Figure 8: Results of facial semantics inference for random masks on CFEED [27].  
We attribute this result to the disentanglement functionality of our proposed emotion inference module. As described in Section 3.2, the emotion inference module disentangles the emotion semantics from the native latent space, enabling the emotion control module to manipulate the latent codes of emotions independently. By contrast, without the emotion inference module, the control module can only manipulate the composite latent codes, resulting in the simultaneous alteration of all facial features.  
We also evaluated the results in the testing set (containing 180 images) using five quantitative terms. The completed results were compared with images that have the same emotions in the dataset. Table 5 presents the evaluation results. It is obvious that EC-GAN with the emotion inference module outperforms EC-GAN without the emotion inference module. This is because the emotion inference module learns different emotion semantics of the face and promotes the model to generate more realistic expression content for the corrupt images.  
Figure 9: Results of ablation study on CFEED [27] with/without the emotion inference module. Model I is with the emotion inference module (EC-GAN) and Model II is without.  
Furthermore, we carried out a quantitative experiment to validate the contributions of the appearance and perceptual losses used in EC-GAN. Three models were trained and compared with the completed EC-GAN model, evaluated using three terms (FID, IS, and SSIM). Table 6 shows the result, where \"\(\check{\vee}\)\" means the model utilizes the loss item. We can see that EC-GAN with both appearance loss and perceptual loss surpasses other models in all metrics, demonstrating that both losses make positive contributions to the final image completion quality and precision.