Robust Face Recognition from Multi-View Videos
Identifieur interne : 004F79 ( Main/Exploration ); précédent : 004F78; suivant : 004F80Robust Face Recognition from Multi-View Videos
Auteurs : MING DU [États-Unis] ; Aswin C. Sankaranarayanan [États-Unis] ; Rama Chellappa [États-Unis]Source :
- IEEE transactions on image processing [ 1057-7149 ] ; 2014.
Descripteurs français
- Pascal (Inist)
- Traitement image, Reconnaissance visage, Image multiple, Redondance, Signal vidéo, Harmonique sphérique, Texture, Mappage, Extraction caractéristique, Algorithme, Similitude, Méthode noyau, Espace Hilbert, Evaluation performance, Base donnée vidéo, Reconnaissance forme, Biométrie, Traitement signal, Reconnaissance automatique, Technologie multi-vues.
- Wicri :
- topic : Biométrie.
English descriptors
- KwdEn :
- Algorithm, Automatic recognition, Biometrics, Face recognition, Feature extraction, Hilbert space, Image processing, Kernel method, Mapping, Multi-view technology, Multiple image, Pattern recognition, Performance evaluation, Redundancy, Signal processing, Similarity, Spherical harmonic, Texture, Video databases, Video signal.
Abstract
Multiview face recognition has become an active research area in the last few years. In this paper, we present an approach for video-based face recognition in camera networks. Our goal is to handle pose variations by exploiting the redundancy in the multiview video data. However, unlike traditional approaches that explicitly estimate the pose of the face, we propose a novel feature for robust face recognition in the presence of diffuse lighting and pose variations. The proposed feature is developed using the spherical harmonic representation of the face texture-mapped onto a sphere; the texture map itself is generated by back-projecting the multiview video data. Video plays an important role in this scenario. First, it provides an automatic and efficient way for feature extraction. Second, the data redundancy renders the recognition algorithm more robust. We measure the similarity between feature sets from different videos using the reproducing kernel Hilbert space. We demonstrate that the proposed approach outperforms traditional algorithms on a multiview video database.
Affiliations:
- États-Unis
- Maryland, Pennsylvanie
- College Park (Maryland), Pittsburgh
- Université Carnegie-Mellon, Université du Maryland
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Le document en format XML
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Sankaranarayanan</name><affiliation wicri:level="4"><inist:fA14 i1="02"><s1>Department of Electrical and Computer Engineering, Carnegie Mellon University</s1><s2>Pittsburgh, PA 15213</s2><s3>USA</s3><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><settlement type="city">Pittsburgh</settlement><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author><author><name sortKey="Chellappa, Rama" sort="Chellappa, Rama" uniqKey="Chellappa R" first="Rama" last="Chellappa">Rama Chellappa</name><affiliation wicri:level="4"><inist:fA14 i1="01"><s1>Center for Automation Research, University of Maryland</s1><s2>College Park, MD 20742</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><settlement type="city">College Park (Maryland)</settlement><region type="state">Maryland</region></placeName><orgName type="university">Université du Maryland</orgName></affiliation></author></analytic><series><title level="j" type="main">IEEE transactions on image processing</title><title level="j" type="abbreviated">IEEE trans. image process.</title><idno type="ISSN">1057-7149</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IEEE transactions on image processing</title><title level="j" type="abbreviated">IEEE trans. image process.</title><idno type="ISSN">1057-7149</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithm</term><term>Automatic recognition</term><term>Biometrics</term><term>Face recognition</term><term>Feature extraction</term><term>Hilbert space</term><term>Image processing</term><term>Kernel method</term><term>Mapping</term><term>Multi-view technology</term><term>Multiple image</term><term>Pattern recognition</term><term>Performance evaluation</term><term>Redundancy</term><term>Signal processing</term><term>Similarity</term><term>Spherical harmonic</term><term>Texture</term><term>Video databases</term><term>Video signal</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Traitement image</term><term>Reconnaissance visage</term><term>Image multiple</term><term>Redondance</term><term>Signal vidéo</term><term>Harmonique sphérique</term><term>Texture</term><term>Mappage</term><term>Extraction caractéristique</term><term>Algorithme</term><term>Similitude</term><term>Méthode noyau</term><term>Espace Hilbert</term><term>Evaluation performance</term><term>Base donnée vidéo</term><term>Reconnaissance forme</term><term>Biométrie</term><term>Traitement signal</term><term>Reconnaissance automatique</term><term>Technologie multi-vues</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Biométrie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Multiview face recognition has become an active research area in the last few years. In this paper, we present an approach for video-based face recognition in camera networks. Our goal is to handle pose variations by exploiting the redundancy in the multiview video data. However, unlike traditional approaches that explicitly estimate the pose of the face, we propose a novel feature for robust face recognition in the presence of diffuse lighting and pose variations. The proposed feature is developed using the spherical harmonic representation of the face texture-mapped onto a sphere; the texture map itself is generated by back-projecting the multiview video data. Video plays an important role in this scenario. First, it provides an automatic and efficient way for feature extraction. Second, the data redundancy renders the recognition algorithm more robust. We measure the similarity between feature sets from different videos using the reproducing kernel Hilbert space. We demonstrate that the proposed approach outperforms traditional algorithms on a multiview video database.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Maryland</li><li>Pennsylvanie</li></region><settlement><li>College Park (Maryland)</li><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li><li>Université du Maryland</li></orgName></list><tree><country name="États-Unis"><region name="Maryland"><name sortKey="Ming Du" sort="Ming Du" uniqKey="Ming Du" last="Ming Du">MING DU</name></region><name sortKey="Chellappa, Rama" sort="Chellappa, Rama" uniqKey="Chellappa R" first="Rama" last="Chellappa">Rama Chellappa</name><name sortKey="Sankaranarayanan, Aswin C" sort="Sankaranarayanan, Aswin C" uniqKey="Sankaranarayanan A" first="Aswin C." last="Sankaranarayanan">Aswin C. Sankaranarayanan</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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