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| Paper IPM / Cognitive / 8809 |
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| Abstract: | |||||||
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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 teacherdirected
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.
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