The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosted manifold principal angles for image set-based recognition
Pattern Recognition
Grassmann discriminant analysis: a unifying view on subspace-based learning
Proceedings of the 25th international conference on Machine learning
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
The kernel orthogonal mutual subspace method and its application to 3D object recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A framework for 3d object recognition using the kernel constrained mutual subspace method
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
On-line learning of mutually orthogonal subspaces for face recognition by image sets
IEEE Transactions on Image Processing
Facing scalability: Naming faces in an online social network
Pattern Recognition
Advances in matrix manifolds for computer vision
Image and Vision Computing
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Generalized mutual subspace based methods for image set classification
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
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We address the problem of face recognition from image sets, where subject-specific subspaces instead of image vectors are compared. Previous methods based on Grassmannian subspace distances mainly take linear subspaces as input. The non-linearity exists when the input data contain complex structure such as pose changes. We generalize Grassmannian distances into high dimensional feature space with kernel trick to handle the underlying non-linearity in data. We show that kernel Grassmannian distances in feature space can be implicitly computed from the input data. Furthermore, we propose to use projection kernel in feature space for discriminant analysis. Comparisons with several state-of-the-art methods were performed on two databases, CMU PIE and YaleB. The proposed methods have demonstrated promising performance.