Improving Detection And Recognition Of Degraded Faces By Discriminative Feature Restoration Using GAN

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Abstract

Face detection and recognition in the wild is currently one of the most interesting and challenging problems. Many al- gorithms with high performance have already been proposed and applied in real-world applications. However, the prob- lem of detecting and recognising degraded faces from low- quality images and videos mostly remains unsolved. In this paper, we present an algorithm capable of recovering facial features from very low quality videos and images. The re- sulting output image boosts the performance of existing face detection and recognition algorithms. It contains an effec- tive method involving metric learning and different loss func- tion components operating on different parts of the generator. This enhances the degraded faces by restoring their lost fea- tures rather than its perceptual quality. Our approach has been experimentally proven to enhance face detection and recogni- tion, e.g., the face detection rate is improved by 3.08% for S3FD [1] and the area under the ROC curve for recognition is improved by 2.55% for ArcFace [2] on the SCFace dataset.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing (ICIP): Proceedings
Publisher IEEE
Pages2146-2150
Number of pages5
ISBN (Electronic)978-1-7281-6395-6
ISBN (Print)978-1-7281-6396-3
DOIs
Publication statusPublished - 30 Sep 2020

Publication series

NameIEEE International Conference on Image Processing (ICIP): Proceedings
PublisherIEEE
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

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