Grassmann Registration Manifolds for Face Recognition [chapter]

Yui Man Lui, J. Ross Beveridge
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
more » ... that the proposed method yields excellent results using both holistic and local features. Specifically, on the FERET Dup2 data set, our proposed method achieves 83.8% rank 1 recognition: to our knowledge the currently the best result among all non-trained methods. Evidence is also presented that peak recognition performance is achieved using roughly 100 distinct perturbed images.
doi:10.1007/978-3-540-88688-4_4 fatcat:f5fgpd277fhubcxdldypzo6u6i