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 …

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 …

We address the problem of face recognition from image sets, where subject-specific
subspaces instead of image vectors are compared. Previous methods based on …

We address the problem of face recognition by matching image sets. Each set of face
images is represented by a subspace (or linear manifold) and recognition is carried out by …

In face recognition tasks, the dimension of the sample space is typically larger than the
number of the samples in the training set. As a consequence, the within-class scatter matrix …

In this paper, we address the problem of classifying image sets for face recognition, where
each set contains images belonging to the same subject and typically covering large …

In supervised dimensionality reduction, tensor representations of images have recently been
employed to enhance classification of high dimensional data with small training sets …

Investigates the use of linear and nonlinear principal manifolds for learning low-dimensional
representations for visual recognition. Several leading techniques-principal component …

We propose a novel appearance-based face recognition method called the marginFace
approach. By using average neighborhood margin maximization (ANMM), the face images …

We propose a novel manifold learning approach, called Neighborhood Discriminant
Projection (NDP), for robust face recognition. The purpose of NDP is to preserve the within …