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

Simple item page
dc.contributor.authorBerretti, Stefanoen_US
dc.contributor.authorBimbo, Alberto Delen_US
dc.contributor.authorPala, Pietroen_US
dc.contributor.authorMata, Francisco Josè Silvaen_US
dc.contributor.editorStavros Perantonis and Nikolaos Sapidis and Michela Spagnuolo and Daniel Thalmannen_US
dc.date.accessioned2013-10-21T18:15:19Z
dc.date.available2013-10-21T18:15:19Z
dc.date.issued2008en_US
dc.description.abstractAn 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.en_US
dc.description.seriesinformationEurographics 2008 Workshop on 3D Object Retrievalen_US
dc.identifier.isbn978-3-905674-05-7en_US
dc.identifier.issn1997-0463en_US
dc.identifier.urihttps://doi.org/10.2312/3DOR/3DOR08/057-064en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Curve, surface, solid, and object representationsen_US
dc.titleFace Recognition by SVMs Classification and Manifold Learning of 2D and 3D Radial Geodesic Distancesen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
057-064.pdf
Size:
308.43 KB
Format:
Adobe Portable Document Format
Download
Collections
3DOR 08