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

We propose a new model for view-independent face recognition by multiview approach. 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 our model, instead of leaving the ME to partition the face space automatically, the ME is directed to adapt to a particular partitioning corresponding to predetermined views. To force an expert towards a specific view of face, in the representation layer, we provide each expert with its own eigenspace computed from the faces in the corresponding view. 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 expert are updated. The experimental results support our claim that directing the experts to a predetermined partitioning of face space improves the performance of the conventional ME for view-independent face recognition. In particular, for 1200 images of unseen intermediate views of faces from 20 subjects, the ME with single-view eigenspaces yields the average recognition rate of 80.51% in 10 trials, which is noticeably increased to 90.29% by applying the TDL method.