Teacher-directed learning in view-independent face recognition with mixture of experts using overlapping eigenspaces

A model for view-independent face recognition, based on Mixture of Experts, ME, is presented. In the basic form of ME the problem space is automatically divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our proposed model, the ME is directed to adapt to a particular partitioning corresponding to predetermined views. To force an expert towards a particular partitioning corresponding to predetermined views, a new representation scheme, overlapping eigenspaces, is introduced, that provides each expert with an eigenspace computed from the faces in the corresponding neighboring views. Furthermore, we use teacher-directed learning, TDL, in a way that according to the pose of the input training sample, only the weights of the corresponding experts are updated. The experimental results support our claim that directing the experts to a predetermined partitioning of the face space improves the performance of the conventional ME for view-independent face recognition. Comparison with some of the most related methods indicates that the proposed model yields excellent recognition rate in view-independent face recognition.