A HETEROGENEOUS FACE RECOGNITION VIA PART ADAPTIVE AND RELATION ATTENTION MODULE

Rushuang Xu, Myeong Ah Cho, Sangyoun Lee

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

Abstract

In the face recognition application scenario, we need to process facial images captured in various conditions, such as at night by near-infrared (NIR) surveillance cameras. The illumination difference between NIR and visible-light (VIS) images causes a domain gap, and the variations in pose and emotion also make facial matching more difficult. Since heterogeneous face recognition (HFR) has difficulties in domain discrepancy, many studies have focused on extracting domain-invariant features, such as facial part relational information. However, when pose variation occurs, the facial component position changes and a different part relation is extracted. In this paper, we propose a part relation attention module that crops facial parts obtained through a semantic mask and performs relational modeling using each of these representative features. Furthermore, we suggest component adaptive triplet loss using adaptive weights for each part to reduce the intra-class distance regardless of the domain as well as pose. Finally, our method exhibits a performance improvement in the CASIA NIR-VIS 2.0 [1] and achieves superior results in the BUAA-VisNir [2] with large pose and emotion variations.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages2983-2987
Number of pages5
ISBN (Electronic)9781665441155
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 2021 Sept 192021 Sept 22

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September
ISSN (Print)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period21/9/1921/9/22

Bibliographical note

Publisher Copyright:
© 2021 IEEE

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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