The nature of statistical learning theory
The nature of statistical learning theory
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
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
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Grassmannian distances and discriminant analysis for face recognition from image sets
Pattern Recognition Letters
On-line learning of mutually orthogonal subspaces for face recognition by image sets
IEEE Transactions on Image Processing
Object recognition with hierarchical kernel descriptors
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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The subspace-based methods are effectively applied to classify sets of feature vectors by modeling them as subspaces. It is, however, difficult to appropriately determine the subspace dimensionality in advance for better performance. For alleviating such issue, we present a generalized mutual subspace method by introducing soft weighting across the basis vectors of the subspace. The bases are effectively combined via the soft weights to measure the subspace similarities (angles) without definitely setting the subspace dimensionality. By using the soft weighting, we consequently propose a novel mutual subspace-based method to construct the discriminative space which renders more discriminative subspace similarities. In the experiments on 3D object recognition using image sets, the proposed methods exhibit stably favorable performances compared to the other subspace-based methods.