On-line learning of mutually orthogonal subspaces for face recognition by image sets.

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 subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.

Contrasting high-resolution characteristic shock-capturing methods in aeroacoustic test problems

Two shock-capturing methods are considered. One is based on a standard conservative Roe scheme with van Leer's MUSCL variable extrapolation method applied to characteristic variables and a Runge-Kutta time stepping scheme. The other is based on the novel CABARET space-time scheme, which uses two sets of staggered variables, one for the conservation step and the other for characteristic splitting into local Riemann invariants. The methods are compared in a range of 2-D inviscid compressible flow test cases. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.