Abstract
One sample per person face recognition problem is a challenging problem in face recognition. The authors propose a basis plus variety model on a high-dimensional unit sphere to tackle the problem. In the model, the query image is approximately a linear combination of the basis image and variety image. The basis images are the neutral images of subjects and variety images are generalised from multi-sample subjects in the gallery. Particle swarm optimisation is chosen to find out the optimum combination of the basis and variety in terms of the minimum L2 distance relative to the query image. The identity of the query image is equal to the identity of the basis image of the optimum combination. Experiments on the extended Yale face database B and AR database are provided to show the validity of the proposed algorithm.
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