High-Resolution Face Recognition Via Deep Pore-Feature Matching

Shun Cheung Lai, Minna Kong, Kin Man Lam, Dong Li

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

1 Citation (Scopus)

Abstract

Because of the advancement of capturing devices, both image resolution and image quality have been significantly improved. Efficiently utilizing facial information is beneficial in enhancing the performance of face recognition methods. For high-resolution face images, pore-scale facial features can be observed. The positions and local patterns of pore features are biologically discriminative, so they can be explored for face identification. In this paper, we extend the previous work on pore-scale features, by proposing a new learning-based descriptor, namely PoreNet. Experiment results show that our proposed descriptor achieves an excellent performance on two high-resolution face datasets, namely Bosphorus and MultiPIE. More importantly, our proposed method significantly outperforms the state-of-the-art Convolutional Neural Network (CNN)-based face recognition method, when query faces are highly occluded. The code of our proposed method is available at: https://github.com/johnnysclai/PoreNet.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages3477-3481
Number of pages5
ISBN (Electronic)9781538662496
DOIs
Publication statusPublished - 22 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

  • Face recognition
  • feature extraction
  • high-resolution face recognition
  • pore-scale facial feature

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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