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    Face Recognition by SVMs Classification and Manifold Learning of 2D and 3D Radial Geodesic Distances

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    Date
    2008
    Author
    Berretti, Stefano ORCID
    Bimbo, Alberto Del
    Pala, Pietro
    Mata, Francisco Josè Silva
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    Abstract
    An original face recognition approach based on 2D and 3D Radial Geodesic Distances (RGDs), respectively computed on 2D face images and 3D face models, is proposed in this work. In 3D, the RGD of a generic point of a 3D face surface is computed as the length of the particular geodesic that connects the point with a reference point along a radial direction. In 2D, the RGD of a face image pixel with respect to a reference pixel accounts for the difference of gray level intensities of the two pixels and the Euclidean distance between them. Support Vector Machines (SVMs) are used to perform face recognition using 2D- and 3D-RGDs. Due to the high dimensionality of face representations based on RGDs, embedding into lower-dimensional spaces using manifold learning is applied before SVMs classification. Experimental results are reported for 3D-3D and 2D-3D face recognition using the proposed approach.
    BibTeX
    @inproceedings {10.2312:3DOR:3DOR08:057-064,
    booktitle = {Eurographics 2008 Workshop on 3D Object Retrieval},
    editor = {Stavros Perantonis and Nikolaos Sapidis and Michela Spagnuolo and Daniel Thalmann},
    title = {{Face Recognition by SVMs Classification and Manifold Learning of 2D and 3D Radial Geodesic Distances}},
    author = {Berretti, Stefano and Bimbo, Alberto Del and Pala, Pietro and Mata, Francisco Josè Silva},
    year = {2008},
    publisher = {The Eurographics Association},
    ISSN = {1997-0463},
    ISBN = {978-3-905674-05-7},
    DOI = {10.2312/3DOR/3DOR08/057-064}
    }
    URI
    http://dx.doi.org/10.2312/3DOR/3DOR08/057-064
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
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