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Article

Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences

Department of Telecommunications, Brno University of Technology, 616 00 Brno, Czech Republic
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Appl. Sci. 2020, 10(20), 7213; https://doi.org/10.3390/app10207213
Received: 25 August 2020 / Revised: 30 September 2020 / Accepted: 10 October 2020 / Published: 16 October 2020
(This article belongs to the Special Issue Deep Image Semantic Segmentation and Recognition)
Forensically trained facial reviewers are still considered as one of the most accurate approaches for person identification from video records. The human brain can utilize information, not just from a single image, but also from a sequence of images (i.e., videos), and even in the case of low-quality records or a long distance from a camera, it can accurately identify a given person. Unfortunately, in many cases, a single still image is needed. An example of such a case is a police search that is about to be announced in newspapers. This paper introduces a face database obtained from real environment counting in 17,426 sequences of images. The dataset includes persons of various races and ages and also different environments, different lighting conditions or camera device types. This paper also introduces a new multi-frame face super-resolution method and compares this method with the state-of-the-art single-frame and multi-frame super-resolution methods. We prove that the proposed method increases the quality of face images, even in cases of low-resolution low-quality input images, and provides better results than single-frame approaches that are still considered the best in this area. Quality of face images was evaluated using several objective mathematical methods, and also subjective ones, by several volunteers. The source code and the dataset were released and the experiment is fully reproducible. View Full-Text
Keywords: face recognition; super resolution; multi frame; image processing; database; dataset; sequences; deep learning face recognition; super resolution; multi frame; image processing; database; dataset; sequences; deep learning
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MDPI and ACS Style

Rajnoha, M.; Mezina, A.; Burget, R. Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences. Appl. Sci. 2020, 10, 7213. https://doi.org/10.3390/app10207213

AMA Style

Rajnoha M, Mezina A, Burget R. Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences. Applied Sciences. 2020; 10(20):7213. https://doi.org/10.3390/app10207213

Chicago/Turabian Style

Rajnoha, Martin, Anzhelika Mezina, and Radim Burget. 2020. "Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences" Applied Sciences 10, no. 20: 7213. https://doi.org/10.3390/app10207213

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