Improving mixture of experts for view-independent face recognition using teacher-directed learning

In this paper we develop a new learning method, called teacher-directed learning (TDL), for mixture of experts (ME) to perform view-independent face recognition. 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 method, the ME is directed to adapt to a particular partitioning corresponding to predetermined views. To do this, we apply a new learning method to ME, called TDL, in a way that according to the pose of the input training sample, only the weights of the corresponding experts are updated. We apply TDL to MEs, composed of MLP experts and a radial basis function gating network, with different representation schemes: global, single-view and overlapping eigenspace. We test them with previously intermediate unseen views of faces. 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.