Weighted sparse representation using a learned distance metric for face recognition

Xiaochao Qu, Suah Kim, Dessalegn Atnafu, Hyong Joong Kim

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

1 Citation (Scopus)

Abstract

This paper presents a novel weighted sparse representation classification for face recognition with a learned distance metric (WSRC-LDM) which learns a Mahalanobis distance to calculate the weight and code the testing face. The Mahalanobis distance is learned by using the information-theoretic metric learning (ITML) which helps to define a better weight used in WSRC. In the meantime, the learned distance metric takes advantage of the classification rule of SRC which helps the proposed method classify more accurately. Extensive experiments verify the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - International Conference on Image Processing, ICIP
PublisherIEEE Computer Society
Pages4594-4598
Number of pages5
Volume2015-December
ISBN (Print)9781479983391
DOIs
Publication statusPublished - 2015 Dec 9
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 2015 Sep 272015 Sep 30

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period15/9/2715/9/30

Keywords

  • Face Recognition
  • Metric Learning
  • Weighted Sparse Representation Classification

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Qu, X., Kim, S., Atnafu, D., & Kim, H. J. (2015). Weighted sparse representation using a learned distance metric for face recognition. In Proceedings - International Conference on Image Processing, ICIP (Vol. 2015-December, pp. 4594-4598). [7351677] IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7351677