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

Simple item page
dc.contributor.authorBerretti, Stefanoen_US
dc.contributor.authorAmor, Boulbaba Benen_US
dc.contributor.authorDaoudi, Mohameden_US
dc.contributor.authorBimbo, Alberto Delen_US
dc.contributor.editorMohamed Daoudi and Tobias Schrecken_US
dc.date.accessioned2013-10-21T16:10:02Z
dc.date.available2013-10-21T16:10:02Z
dc.date.issued2010en_US
dc.description.abstractFacial 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.en_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrievalen_US
dc.identifier.isbn978-3-905674-22-4en_US
dc.identifier.issn1997-0471en_US
dc.identifier.urihttps://doi.org/10.2312/3DOR/3DOR10/047-054en_US
dc.publisherThe Eurographics Associationen_US
dc.titlePerson Independent 3D Facial Expression Recognition by a Selected Ensemble of SIFT Descriptorsen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
047-054.pdf
Size:
355.01 KB
Format:
Adobe Portable Document Format
Download
Collections
3DOR 10