View-based eigenspaces with mixture of experts for view-independent face recognition

Abstract

We propose a new model for view-independent face recognition, which lies under the category of multi-view approaches. We use the so-called "mixture of experts", ME, in which, the problem space is divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In the proposed model, instead of allowing ME to partition the face space automatically, the ME is directed to adapt to a particular partitioning corresponding to predetermined views. In this model, view-dependent representations are used to direct the experts towards a specific area of face space. The experimental results support our claim that directing the mixture of experts to a predetermined partitioning of face space is a more beneficial way of using conventional ME for view-independent face recognition. © Springer-Verlag Berlin Heidelberg 2007.