A Mixture of Multilayer Perceptron Experts Network for Modeling Face/Nonface Recognition in Cortical Face Processing Regions

Recent studies in neurobiology and especially in neuroimaging report that a gating mechanism prior to face processing levels of human visual system, facilitates the face/nonface recognition task. In accordance to these biological evidences, we propose a face/nonface recognition model which makes use of mixture of experts network. In order to improve the face/nonface recognition accuracy, the outputs of the expert networks are combined using a gating network. A novel structure, which is the use of multilayer perceptrons (MLPs) in forming the expert networks, is introduced. The learning algorithm is modified to be adapted with the MLP networks. The results reveal that using a mixture of simple MLPs is much more beneficial, in many respects, as it shows more certainty at its output and is also easier to train than a single, but complex, MLP.