Principal component analysis of image gradient orientations for face recognition

Georgios Tzimiropoulos, Stefanos Zafeiriou, Maja Pantic

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    32 Citations (Scopus)


    We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the _2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard _2 intensitybased PCA. We demonstrate some of its favorable properties for the application of face recognition.
    Original languageUndefined
    Title of host publicationProceedings of the IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011)
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Number of pages6
    ISBN (Print)978-1-4244-9140-7
    Publication statusPublished - Mar 2011
    Event9th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2011 - Santa Barbara, United States
    Duration: 21 Mar 201125 Mar 2011
    Conference number: 9

    Publication series

    PublisherIEEE Computer Society


    Conference9th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2011
    Abbreviated titleFG
    Country/TerritoryUnited States
    CitySanta Barbara


    • METIS-285026
    • IR-79432
    • Generators
    • Image reconstruction
    • Robustness
    • Face
    • Principal component analysis
    • Pixel
    • EWI-21324
    • EC Grant Agreement nr.: FP7/231287
    • Lighting

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