DIRA: Disjoint-Identity Resolution Adaptation for Low-Resolution Face Recognition

Jacky Chen Long Chai, Cheng Yaw Low, Andrew Beng Jin Teoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Low-resolution face recognition (LRFR) intends to identify unknown poor-quality face images and is widely employed in real-world surveillance applications. While collecting a large-scale labeled low-resolution (LR) face dataset could be conducive, it is practically infeasible due to labor costs and privacy issues. In contrast, accessing high-resolution (HR) face datasets is relatively effortless. However, prevailing domain adaptation techniques are often tenuous as they demand sharing of similar face images at different resolutions. We propose disjoint-identity resolution adaptation (DIRA) to transfer substantial face semantic representations from HR to LR face images, despite disjoint identities and limited labeled LR images. We accredit that continuous adversarial learning between HR-LR resolution alignment and segregation renders effective feature extraction and discriminative LR face representation. Our experimental results show a notable performance boost over the recent state-of-the-art methods for the challenging realistic low-resolution face recognition task.

Original languageEnglish
Title of host publicationFourteenth International Conference on Digital Image Processing, ICDIP 2022
EditorsXudong Jiang, Wenbing Tao, Deze Zeng, Yi Xie
ISBN (Electronic)9781510657564
Publication statusPublished - 2022
Event14th International Conference on Digital Image Processing, ICDIP 2022 - Wuhan, China
Duration: 2022 May 202022 May 23

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference14th International Conference on Digital Image Processing, ICDIP 2022

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NO. NRF-2022R1A2C1010710).

Publisher Copyright:
© 2022 SPIE.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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