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Image and Vision Computing
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Subclass linear discriminant analysis for video-based face recognition
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ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Face recognition from images with high pose variations by transform vector quantization
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Orthogonal discriminant vector for face recognition across pose
Pattern Recognition
Multi-eigenspace learning for video-based face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Hyperdisk based large margin classifier
Pattern Recognition
Patch-Based bag of features for face recognition in videos
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Face recognition in videos: a graph based modified kernel discriminant analysis
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Multi-local model image set matching based on domain description
Pattern Recognition
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the ambient image space. The complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a K-means algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihood that the input image comes from a particular pose manifold and the transition probability to this pose manifold from the previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing frame-based face recognition methods with temporal voting schemes.