Abstract:

Images of faces, represented as high-dimensional pixel arrays, often belong to a manifold of intrinsically low dimension. Face recognition, and computer vision research in general, has witnessed a growing interest in techniques that capitalize on this observation, and apply algebraic and statistical tools for extraction and analysis of the underlying manifold. In this chapter we describe in roughly chronological order techniques that identify, parameterize and analyze linear and nonlinear subspaces, from the original Eigenfaces technique to the recently introduced Bayesian method for probabilistic similarity analysis, and discuss comparative experimental evaluation of some of these techniques. We also discuss practical issues related to the application of subspace methods for varying pose, illumination and expression.