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    Person Independent 3D Facial Expression Recognition by a Selected Ensemble of SIFT Descriptors

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    Date
    2010
    Author
    Berretti, Stefano
    Amor, Boulbaba Ben
    Daoudi, Mohamed
    Bimbo, Alberto Del
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    Abstract
    Facial expression recognition has been addressed mainly working on 2D images or videos. In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original approach is proposed that relies on selecting the minimal-redundancy maximal-relevance features derived from a pool of SIFT feature descriptors computed in correspondence with facial landmarks of depth images. Training a Support Vector Machine for every basic facial expression to be recognized, and combining them to form a multiclass classifier, an average recognition rate of 77.5% on the BU-3DFE database has been obtained. Comparison with competitors approaches using a common experimental setting on the BU-3DFE database, shows that our solution is able to obtain state of the art results.
    BibTeX
    @inproceedings {3DOR:3DOR10:047-054,
    booktitle = {Eurographics Workshop on 3D Object Retrieval},
    editor = {Mohamed Daoudi and Tobias Schreck},
    title = {{Person Independent 3D Facial Expression Recognition by a Selected Ensemble of SIFT Descriptors}},
    author = {Berretti, Stefano and Amor, Boulbaba Ben and Daoudi, Mohamed and Bimbo, Alberto Del},
    year = {2010},
    publisher = {The Eurographics Association},
    ISSN = {1997-0471},
    ISBN = {978-3-905674-22-4},
    DOI = {10.2312/3DOR/3DOR10/047-054}
    }
    URI
    http://dx.doi.org/10.2312/3DOR/3DOR10/047-054
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    Eurographics Association copyright © 2013 - 2020 
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    Theme by @mire NV
    System hosted at  Graz University of Technology.
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