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    Face components detection using SURF descriptor and SVMs


    Kim, Donghoom and Dahyot, Rozenn (2008) Face components detection using SURF descriptor and SVMs. 2008 International Machine Vision and Image Processing Conference.

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    Abstract

    We present a feature-based method to classify salient points as belonging to objects in the face or background classes. We use SURF local descriptors (speeded up robust features) to generate feature vectors and use SVMs (support vector machines) as classifiers. Our system consists of a two-layer hierarchy of SVMs classifiers. On the first layer, a single classifier checks whether feature vectors are from face images or not. On the second layer, component labeling is operated using each component classifier of eye, mouth, and nose. This approach has the advantage about operating time because windows scanning procedure is not needed. Finally, this system performs the procedure to apply geometrical constraints to labeled descriptors. We show experimentally the efficiency of our approach.

    Item Type: Article
    Keywords: Face Components; Detection; SURF Descriptors; SVMs;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15287
    Identification Number: https://doi.org/10.1109/IMVIP.2008.15
    Depositing User: Rozenn Dahyot
    Date Deposited: 19 Jan 2022 13:09
    Journal or Publication Title: 2008 International Machine Vision and Image Processing Conference
    Publisher: IEEE
    Refereed: Yes
    URI:

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