Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition

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Deep Learning on Lie Groups for Skeleton-Based Action Recognition (2016, methodology): “In particular, inspired by the classical manifold learning theory [38, 36, 4, 12, 20, 19], we equip the new network structure with rotation mapping layers, with which the input Lie group features are transformed to new ones with better alignment.”
Quality Aware Network for Set to Set Recognition (2017, background): “The first approach takes image set as a convex hull [2], affine hull [10] or subspace [1,13].”
Building Deep Networks on Grassmann Manifolds (2016, methodology): “More recently, a new learning scheme was proposed by (Huang et al. 2015b) to perform a geometry-aware dimensionality reduction from the original Grassmann manifold to another lower-dimensional, more discriminative Grassmann manifold.”
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification (2015, methodology): “However, the work (Jung et al., 2012) and our previous work (Huang et al., 2015c) learn the manifold-manifold mapping on another class of Riemannian manifold (i.e., sphere or Grassmann manifold).”
Unconstrained face verification using deep CNN features (2015, background): “[14] learned a projection metric over a set of labeled images which preserves the underlying manifold structure.”