## 3 Results
### Color Correction Evaluation  
As shown in Figure 4, the images produced by cGAN-TV tended to have a similar skin tone. Hence, a color correction was required to make the photo images more realistic and more in accordance with the ground truth. By applying the configuration contained in Table 2, the resulting image could be more visually similar to the original image.  
The first skin tone configuration applied for color correction was for pale white, where the color transfer was not required due to the satisfying results of this color category. We only needed to set the gamma at 0.8. As shown in Figure 6, this configuration succeeded in producing the skin tone of the resulting image that was more similar to the skin tone of the original image.  
The second configuration was fair, in which the color transfer was carried out, and the gamma was set to 0.85. In this case, the color transfer must be performed, because the gamma change only adjusts the exposure of the image and cannot change the skin tone. However, when we only performed the color transfer, the resulting images were too dark and unnatural. Therefore, by using gamma 0.8, the resulting images looked more natural, with the skin tone turning yellowish, as shown in Figure 7.  
Figure 5: Results of cGAN-TV on the testing data.  
Figure 6: Results of pale white configuration in correcting the color of predicted images. The values below the images represent SSIM/FID. The ground truth images are from generated photos [17] and volunteers (private collection).  
As shown in Figure 7, the third or tan configuration used color transfer and changed the gamma to 1.2. As with the fair configuration, this configuration also used two stages, namely color transfer, and gamma changes. The gamma of the image was increased to 1.2. The effect of the tan configuration can be seen in Figure 8.  
Table 5 shows the result evaluation of cGAN-TV with the implementation of color correction. Although the color correction visually gives a more realistic result, the SSIM value of the color correction implementation decreased from 0.83 to 0.76. This was caused by the SSIM calculation process, which begins with converting the image to grayscale. Since the image has undergone a gamma change, the grayscale result is different for the one without the gamma change. This inappropriate grayscale image causes the image structure to not be in accordance with the original image. However, the decrease in SSIM visually did not have the effect of reducing the visual quality of the results, therefore, it was still tolerable. On the other hand, the FID value increased by 16% from 94,705 to 78,944. This is because the FID evaluation takes color similarity into account, and with color correction, the image between the GAN result and the original image has a more similar distribution when compared to that without color correction. Overall, the implementation of color correction provided a more realistic image result and a better FID value. This is prioritized over a high SSIM value because the improvement in visual quality produced is more helpful for ordinary users (in this case the Indonesian people) in assessing the similarity of faces between two images.  
Figure 8: Results of tan configuration in correcting the color of predicted images. The ground truth images are from generated photos [17].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **SSIM** & **FID** \\ \hline Without Color Correction & 0.83 & 94.705 \\ With Color Correction & 0.76 & 78.944 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of cGAN-TV with and without color correction.  
Figure 7: Results of fair configuration in correcting the color of predicted images. The values below the images represent SSIM/FID. The ground truth images are from generated photos [17].