Advances in matrix manifolds for computer vision
Image and Vision Computing
Semi-intrinsic mean shift on riemannian manifolds
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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
Partial least squares regression on grassmannian manifold for emotion recognition
Proceedings of the 15th ACM on International conference on multimodal interaction
Kernel analysis on Grassmann manifolds for action recognition
Pattern Recognition Letters
Fusing cluster-centric feature similarities for face recognition in video sequences
Pattern Recognition Letters
Multi-local model image set matching based on domain description
Pattern Recognition
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A convenient way of dealing with image sets is to represent them as points on Grassmannian manifolds. While several recent studies explored the applicability of discriminant analysis on such manifolds, the conventional formalism of discriminant analysis suffers from not considering the local structure of the data. We propose a discriminant analysis approach on Grassmannian manifolds, based on a graph-embedding framework. We show that by introducing within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, the geometrical structure of data can be exploited. Experiments on several image datasets (PIE, BANCA, MoBo, ETH-80) show that the proposed algorithm obtains considerable improvements in discrimination accuracy, in comparison to three recent methods: Grassmann Discriminant Analysis (GDA), Kernel GDA, and the kernel version of Affine Hull Image Set Distance. We further propose a Grassmannian kernel, based on canonical correlation between subspaces, which can increase discrimination accuracy when used in combination with previous Grassmannian kernels.