# Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences
## 5 Discussion
### Face Recognition Based Metrics  
For all 2500 test images, the face recognition similarity rate index (FR rate (all)) was computed. The same metric was also computed for the subset of the test dataset where face recognition was successful (1518 samples). Only those samples are considered to be successful, where all the models created such a resulting image, which succeeded with face recognition. There was an assumption that the methods with better face features extraction fail rate (lower FR failed) will have a better face recognition similarity rate index on those subset images instead of the whole test set. However, according to achieved results, it seems that this has only a minimal effect.  
A more interesting metric is the face features extraction fail rate (FR failed), i.e., in how many cases it was not possible to extract successfully face features. According to the results, the U-Net+GEU2 method has the lowest number of cases where face recognition failed. It was also examined in how many cases it failed on the same images. The number of different images that are not in the set of failed images of the U-Net+GEU2 method is used to make the output more interpretable. These values are shown as the first value in Table 5. The second value after the slash represents the overall difference from the U-Net+GEU2 FR failed metric.  
The best match of failed images is paradoxically for the bilinear interpolation. On the other hand, the bilinear interpolation failed in 693 cases instead of the U-Net+GEU2 method that failed in 259 cases (the difference is 434) and the bilinear interpolation set of failed images is big enough to match the majority of the U-Net+GEU2 set of images. An interesting finding is an opposite issue. The U-Net+GEU3 method has almost the same number of FR failed metric (the difference is only 11 cases), but its set of images differs in 55 cases compared to the U-Net+GEU2 method.  
After further examination of the method, there were no patterns found (the same interpolations, compression, images background, etc.), which would explain why there are so different results. However, there are some diversities between U-Net+GEU2 and U-Net+GEU3 methods in some cases. Figure 11(a) shows a detailed view of the resulting quality created using U-Net+GEU2 and U-Net+GEU3. Surprisingly, there are also contrary cases, but they are not so significant as illustrated in Figure 11(b). U-Net+GEU3 method failed in face features extraction in both cases. There are probably two essential explanations. First, human perception differs from the machine learning image processing and how the quality of the input image is assessed. Second, the well-known and used Face-recognition framework does not work as well as presented, at least for this case. Among the images which failed in face features extraction are a couple of profile images (see examples of labels in Figure 11(c)). The face features extraction works in this case, but with what accuracy? How do face landmark estimations and face transformations work in these cases?  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Differences** \\ \hline bicubic & 12/415 \\ \hline bilinear & 9/434 \\ \hline lanczos & 16/441 \\ \hline EDSR & 18/442 \\ \hline SRCNN & 11/384 \\ \hline SRGAN & 15/512 \\ \hline ESRGAN & 35/211 \\ \hline U-Net+GEU & 38/58 \\ \hline U-Net+GEU+filter & 16/135 \\ \hline U-Net+GEU3 & 55/11 \\ \hline U-Net+ResBlock & 10/322 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Face recognition (FR) failed differences compared to the U-Net+GEU2 method.