Multi-view Feature Selection for Heterogeneous Face Recognition

Jie Gui, Ping Li

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

8 Scopus citations

Abstract

While the task of feature selection has been studied for many years, the topic of multi-view feature selection for heterogeneous face recognition (HFR) such as visible (VIS) image versus near infrared (NIR) image recognition, photo versus sketch recognition, and face recognition across pose, is rarely studied. In this paper, we propose a multi-view feature selection method (MvFS) for HFR. To the best of our knowledge, MvFS is the first algorithm to address the problem of multiview feature selection for HFR, in which the dimensionalities of different views are the same and the number of selected features of different views are the same. The proposed algorithm is simple and computationally efficient. Our experiments confirm the effectiveness of MvFS.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Data Mining, ICDM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages983-988
Number of pages6
ISBN (Electronic)9781538691588
DOIs
StatePublished - Dec 27 2018
Externally publishedYes
Event18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
Duration: Nov 17 2018Nov 20 2018

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2018-November
ISSN (Print)1550-4786

Conference

Conference18th IEEE International Conference on Data Mining, ICDM 2018
Country/TerritorySingapore
CitySingapore
Period11/17/1811/20/18

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Heterogeneous face recognition
  • Multi-view discriminant analysis
  • Multi-view feature selection

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