Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares

M. Uzair, Arif Mahmood, Ajmal Mian

    Research output: Chapter in Book/Conference paperConference paperpeer-review

    61 Citations (Scopus)


    Hyperspectral imaging offers new opportunities for inter-person facial discrimination. However, compact and discriminative feature extraction from high dimensional hyperspectral image cubes is a challenging task. We propose a spatio-spectral feature extraction method based on the 3D Discrete Cosine Transform (3D-DCT). The 3D-DCT optimally compacts information in the low frequency coefficients. Therefore, we represent each hyperspectral facial cube by a small number of low frequency DCT coefficients and formulate Partial Least Square (PLS) regression for accurate classification. The proposed algorithm is evaluated on three standard hyperspectral face databases. Experimental results show that the proposed algorithm outperforms five current state of the art hyperspectral face recognition algorithms by a significant margin.
    Original languageEnglish
    Title of host publicationProceedings of the British Machine Vision Conference 2013
    Place of PublicationBristol, UK
    Publication statusPublished - 2013
    EventBritish Machine Vision Conference - Bristol, UK, Bristol, United Kingdom
    Duration: 9 Sep 201313 Sep 2013


    ConferenceBritish Machine Vision Conference
    Abbreviated titleBMVC
    Country/TerritoryUnited Kingdom


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