Elements of information theory
Elements of information theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel principal component analysis
Advances in kernel methods
Mixtures of probabilistic principal component analyzers
Neural Computation
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Weighted and Robust Incremental Method for Subspace Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Ship identification in sequential ISAR imagery
Machine Vision and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition with Image Sets Using Manifold Density Divergence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Journal of Cognitive Neuroscience
An information-theoretic approach to face recognition from face motion manifolds
Image and Vision Computing
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Decision making in the LDA space: generalised gradient direction metric
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Learning discriminative canonical correlations for object recognition with image sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A view of three decades of linear filtering theory
IEEE Transactions on Information Theory
Video parsing based on head tracking and face recognition
Proceedings of the 6th ACM international conference on Image and video retrieval
A methodology for rapid illumination-invariant face recognition using image processing filters
Computer Vision and Image Understanding
Kernel Grassmannian distances and discriminant analysis for face recognition from image sets
Pattern Recognition Letters
Graph-based classification of multiple observation sets
Pattern Recognition
Kernel discriminant transformation for image set-based face recognition
Pattern Recognition
Randomised manifold forests for principal angle-based face recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
On the behavior of kernel mutual subspace method
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Image set-based hand shape recognition using camera selection driven by multi-class AdaBoosting
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Multi-local model image set matching based on domain description
Pattern Recognition
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In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature.