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Paper   IPM / Cognitive Sciences / 8596
School of Cognitive Sciences
  Title:   View-Based Eigenspaces with Mixture of Experts for View-Independent Face Recognition
1.  R. Ebrahimpour
2.  E. Kabir
3.  M.R. Yousefi
  Status:   Published
  Journal: Lecture Notes in Computer Science
  Vol.:  4472
  Year:  2007
  Pages:   131-140
  Supported by:  IPM
We propose a new model for view-independent face recognition, which lies under the category of multi-view approaches. 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 the proposed model, instead of allowing ME to partition the face space automatically, the ME is directed to adapt to a particular partitioning corresponding to predetermined views. In this model, view-dependent representations are used to direct the experts towards a specific area of face space. The experimental results support our claim that directing the mixture of experts to a predetermined partitioning of face space is a more beneficial way of using conventional ME for view-independent face recognition.

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