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
    Country/TerritoryCanada
    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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