# Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences
## 5 Discussion
### Questionnaire  
The problem of face image super-resolution is significantly more challenging than general super-resolution tasks. It is clear that in every super-resolution method, some particular errors and mistakes must be created since the input image contains less information than there is expected in the resulting image. Thus, some percentages of the pixels are estimated. In the case of general super-resolution methods, a mistake in the image is often hard to be noticed and can be ignored. However, when the image is checked for some details, typically texts, brand logos, or other well-known complex shapes, some errors are apparent at first glance. This is also the case for facial images. For example, if the resulting image of a face is missing an eye, or it has three eyes, it might be for metrics like PSNR or MSE neglectable error, but for human perception, it can be very disruptive. The same situation is with two noses or no nose, the asymmetrical appearance of the eyes or otherwise deformed face.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Threshold** & **0.6** & **0.5** & **0.45** & **0.4** & **0.35** \\ \hline different people & 76 & 2378 & 6651 & 10,822 & 13,369 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different people in the dataset using the Face-recognition framework similarity threshold.  
Another topic that should be taken into consideration is that the main objective is not just to generate images that are good looking. A more important criterion is that the resulting facial image should be of such a quality that the identification must be possible. The studied images are at the borderline where images start being useful for the recognition. When considering each particular low-resolution image from a sequence alone, it is not possible or it is difficult to recognize a person. Nevertheless, the resulting higher resolution image should be of sufficient quality, in which it can be used for this purpose. For this reason, we addressed several volunteers and asked them to fill out the questionnaire. The purpose of the questionnaire was to obtain feedback regarding the quality of the achieved face images from the human perspective. The volunteers saw all the results of the super-resolution methods, and they were asked to mark the result, which they consider is the best.  
The U-Net based methods were their main choices, and the interpolation-based methods were almost not selected. An interesting finding was that the SRGAN method was considered as the worst one. The decisions were in general made based on the essential face features clearness (same eyes, nose, mouth)--the entirety of the face. However, some cases degraded teeth and face borders so much that the preferred image was with a lower quality image but with an entire face. The ESRGAN method was the only method that had significant deformations of the face. Another interesting information was that people intuitively prefer blurred edges instead of sharp edges and no artifacts, even when the image looks good. These feedbacks correlate with the objective results, except for the ESRGAN method. It is the second sharpest method and it has got the most votes among all single-frame methods from the questionnaire.