Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination

Shun Cheung Lai, Chen Hang He, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The state-of-the-art Convolutional Neural Network (CNN)-based methods have achieved promising recognition performance on human face images. However, the accuracy cannot be retained when face images are at very low resolution (LR). In this paper, we propose a novel loss function, called identity-preserved loss, which combines with the image-content loss to jointly supervise CNNs, for performing face hallucination and recognition simultaneously. Therefore, the trained network is able to perform face hallucination and identity preservation, even if the query face is of very low resolution. More importantly, experimental results show that our proposed method can preserve the identities for the LR images from unknown subjects, who are not included in the training set. The source code of our proposed method is available at: https://github.com/johnnysclai/SR-LRFR.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages1173-1177
Number of pages5
ISBN (Electronic)9781538662496
DOIs
Publication statusPublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan
Duration: 22 Sep 201925 Sep 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan
CityTaipei
Period22/09/1925/09/19

Keywords

  • deep learning
  • Face hallucination
  • identity-preserved loss
  • low-resolution face recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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