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Grassmann Registration Manifolds for Face Recognition
[chapter]
2008
Lecture Notes in Computer Science
Motivated by image perturbation and the geometry of manifolds, we present a novel method combining these two elements. First, we form a tangent space from a set of perturbed images and observe that the tangent space admits a vector space structure. Second, we embed the approximated tangent spaces on a Grassmann manifold and employ a chordal distance as the means for comparing subspaces. The matching process is accelerated using a coarse to fine strategy. Experiments on the FERET database
doi:10.1007/978-3-540-88688-4_4
fatcat:f5fgpd277fhubcxdldypzo6u6i