# Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences
## 2 Related Work
### Motivation  
Most of the works described earlier are focused on general image super-resolution. Unfortunately, those methods cannot often be used for person identification since facial super-resolution has slightly different requirements.  
Although the general super-resolution methods often works very well for general images and sometimes they even provide really outstanding results, these methods still create some mistakes in the images. In the case of general images, these errors can often be ignored. However, a mistake in a face image, where an eye or nose is missing or is deformed, even if it might be from the point of pixel error a minor error, from the point of view of human perception, it can be strongly disruptive. In this case, even a slight modification of the image can have a significant impact on human perception of the face and identification of a person, as shown in Figure 2.  
Another issue may be the credibility of the super-resolution methods from a biometric perspective. For biometrics, it is absolutely unacceptable to be creative and generate parts of the image that do not originate from its input.  
The current trend and direction of research of super-resolution methods focus primarily on the general single-frame methods. As the multi-frame methods have more information on its input, it is expected they also probably have a higher potential to reach better results that are based on the input information and are not creatively filling the missing parts of the image. Furthermore, considering the technical capabilities of modern CCTV hardware (e.g., frames independently on fps), it is even possible to go beyond the possibilities of humans and there is an opportunity to reach even better results in the future. Although cognitive skills of the human brain still significantly exceed the skills of machines, narrowly focused artificial intelligence has the potential to bring new applications and, for example, make identification more objective. Examples of these applications could be an automatic search for the best facial image of a suspect for news announcements, reconstruction of the face using a sequence of images, and the suppressing of adverse lighting conditions.  
One of the obstacles in the development of the current facial multi-frame super-resolution methods is the absence of a publicly available dataset, which would be designed for this purpose.  
Many specific requirements are imposed on such a dataset. Data of such a dataset should not be biased, it must be of sufficient size, it must contain records from real-world environments including different lighting or weather conditions, data must be not dependent on a specific video camera hardware, and it should reflect also real-world compression artifacts, the so-called encoding fragments. Contained faces should cover a variety of human races and various ages. In each case, the data must contain a sequence of several faces, not just one frame.  
Regarding available datasets, there is currently no dataset that meets such requirements. Face images used to be biased because of used celebrities, which tends to cause the models to predict \"pretty\" faces suppressing important individual face features (LFW, CelebA). There is a lack of low quality image inputs, especially in the form of sequences (PubFig, FERET) with a high-quality label (Youtube faces DB). All improvements are summarized in Table 1.