Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Generalised Canonical Correlation Analysis
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Comparative Evaluation of Face Sequence Matching for Content-Based Video Access
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Kernel independent component analysis
The Journal of Machine Learning Research
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
Face Recognition with Image Sets Using Hierarchically Extracted Exemplars from Appearance Manifolds
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A New Look at Filtering Techniques for Illumination Invariance in Automatic Face Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Face Set Classification using Maximally Probable Mutual Modes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Robust Hausdorff distance measure for face recognition
Pattern Recognition
Toward a theory of information processing
Signal Processing
Boosted manifold principal angles for image set-based recognition
Pattern Recognition
Statistical Consistency of Kernel Canonical Correlation Analysis
The Journal of Machine Learning Research
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Speech and Language
An information-theoretic approach to face recognition from face motion manifolds
Image and Vision Computing
Converting thermal infrared face images into normal gray-level images
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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
Face recognition with the multiple constrained mutual subspace method
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
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In this paper we address the problem of matching sets of vectors embedded in the same input space. We propose an approach which is motivated by canonical correlation analysis (CCA), a statistical technique which has proven successful in a wide variety of pattern recognition problems. Like CCA when applied to the matching of sets, our extended canonical correlation analysis (E-CCA) aims to extract the most similar modes of variability within two sets. Our first major contribution is the formulation of a principled framework for robust inference of such modes from data in the presence of uncertainty associated with noise and sampling randomness. E-CCA retains the efficiency and closed form computability of CCA, but unlike it, does not possess free parameters which cannot be inferred directly from data (inherent data dimensionality, and the number of canonical correlations used for set similarity computation). Our second major contribution is to show that in contrast to CCA, E-CCA is readily adapted to match sets in a discriminative learning scheme which we call discriminative extended canonical correlation analysis (DE-CCA). Theoretical contributions of this paper are followed by an empirical evaluation of its premises on the task of face recognition from sets of rasterized appearance images. The results demonstrate that our approach, E-CCA, already outperforms both CCA and its quasi-discriminative counterpart constrained CCA (C-CCA), for all values of their free parameters. An even greater improvement is achieved with the discriminative variant, DE-CCA.