# Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences
## 6 Conclusions  
Forensically trained facial reviewers are still considered to be one of the most accurate approaches to person identification, especially in the case of low-resolution or low-quality videos. The human brain can utilize information not just from a single image but also from a sequence of faces (i.e., videos) and, even in the case of low-quality records or a long distance from a camera. They can accurately identify a person. For computer methods, this remains a challenge. However, on the other hand, they have the potential to support human reviewers and help to pre-process the data and extract as much information from the data as possible. One of such a use-case would be, for example, to reconstruct the facial image in higher quality from a video, which can police use for search and announce it in newspapers. This paper introduced a large-scale face dataset containing 17,426 sequences of face images. The dataset covers different races, ages, lighting conditions, and many types of different camera devices. Records were obtained from the real environment, including common defects and imperfections that occur in the videos (e.g., compression artifacts, blur, etc.) According to our knowledge, it is the first  
Figure 12: Examples of low face feature extraction performance. (**a,b**) U-Net + GEU2 (passed) and U-Net + GEU3 (failed) methods comparison. (**c**) Examples of profile face images of database samples that passed face features extraction as well during the database creation filtration step. dataset of a comparable size containing sequences of face images. The paper also introduces a new multi-frame face super-resolution method. This method has been proven to produce better results than single-frame methods which are considered as state-of-the-art, and they are one of the most effective and often compared methods in this research area. Therefore, the hypothesis that the multi-frame method produces better results than the deep learning-based single-frame methods was proven. The results were also evaluated using several objective metrics and also subjectively by volunteers where the U-Net+GEU2 method has the best results regarding human perception, and the U-Net+GEU3 method achieved the best overall score for objective metrics. The proposed method is not generative and it was proven to improve the quality of the face image. The source code and the dataset were released, and the experiment is fully reproducible. The new and unique MLFDB dataset has been published for studying face super-resolution from sequences of images (i.e., multi-frame problem). The benchmark with given rules and metrics was created, and results achieved in this paper were used as an initial starting point and as a challenge to other researchers. They are presented in the leaderboard table as a part of the benchmark. Regarding results themselves, it was found that human perception plays a significant role during model evaluation. Therefore, it is essential to use some subjective human evaluation, for example, in the form of questionnaires or surveys. Conceptualization, M.R. and R.B. ; methodology, A.M.; software, M.R. and A.M.; validation, M.R. and R.B. ; formal analysis, A.M. and M.R. ; investigation, A.M.; resources, R.B. ; data curation, M.R. and A.M.; writing--original draft preparation, M.R. ; writing--review and editing, R.B. ; visualization, M.R. and A.M.; supervision, R.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the Interreg Central Europe niCE-life by the grant CE1581. Research described in this paper was financed by the Interreg Central Europe niCE-life by the grant CE1581. We would like to thank all of the volunteers that attended the questionnaire, especially the ones who provided the feedback. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: