# Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences
## 1 Introduction  
Closed-circuit television (CCTV) is a widely used technology for monitoring public and private places, which helps to increase overall safety, prevent frauds, shoplifting, thefts, burglaries, vandalism, terrorism and others. It is often installed in public areas and businesses throughout the world to prevent the abovementioned crimes and to increase overall public safety. Unfortunately, there are also many abuses and other related questions mainly concerning privacy issues. In relation to police searches and the investigation of serious crimes, there is no doubt that they have a very positive impact on the clarification of criminal offences and early detention of crime offenders [1]. In practice, it often happens that even though the crime is recorded using CCTV, the recording cannot be used for police investigation or a police search and cannot be used as evidence. The cause of this is often the poor quality of the recording, the distance of the camera from the crime place, bad lighting, bad weather conditions, or others factors which cause the person on the recording to beunambiguously identified. Such a record cannot even be used by the police to ask the public for help in assistance with tracing the perpetrator, for instance in newspapers or online media. Most of the computer face recognition systems usually use a single still image and based on it recognize a face. The human experts--forensically trained facial reviewers--are still considered one of the most accurate approaches for person identification. They are generally more resilient to noise in images, and also more efficient in utilizing the information contained in the video, even when the quality of images are poor. They can take into account the dynamics of walk, gestures, fitness status of a person, and possibly other characteristics. They can also utilize the information from a sequence of face images and can be more accurate when compared to recognition from each isolated image. This paper focuses on increasing the quality of the facial image using a video sequence with emphasis on biometric utilization where the principle is illustrated (see Figure 1). The input face image is recorded from a long distance (i.e., its resolution is low and thus a single facial image cannot be used for person identification). This particular example demonstrates the difficulty of person identification from a long distance, which is quite frequent for surveillance cameras. Inspired by human skills, this paper investigates methods that can reconstruct a high-resolution face image from a series of low-resolution and poor quality images. The research described in this paper introduces a couple of contributions, whose novelty or improvement are briefly described by the following points for better clarity:  
* This paper introduces a new methodology for super-resolution (SR) different from the general-purpose one, which is limited to faces and biometric applications. * The sequence of multiple images is processed (multi-frame methods) instead of using just a single image as most existing datasets allow (single-frame methods). * We proposed a methodology that was compared to other state-of-the-art methods, currently, the single-frame super-resolution methods can be considered as one of the best because of the lack of the progress and non-utilization of deep learning in multi-frame super-resolution methods [2; 3]. Moreover, general super-resolution methods are often not applicable for purposes of biometrics, even if they provide good resolution. * A new unique large-scale dataset, containing 17,426 samples of image sequences taken from a wide range of recording devices, is provided. They contain faces of various ages, races, illumination conditions that are taken from real-world environments. * The paper also defines metrics for facial quality measurements that are reproducible and based on open-source solutions. These objective metrics are widely accessible approaches and can boost further research in this area.