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Combining Classifier for Face Identification at Unknown Views with a Single Model Image

  • Tae-Kyun Kim
  • Josef Kittler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

We investigate a number of approaches to pose invariant face recognition. Basically, the methods involve three sequential functions for capturing nonlinear manifolds of face view changes: representation, view-transformation and discrimination. We compare a design in which the three stages are optimized separately, with two techniques which establish the overall transformation by a single stage optimization process. In addition we also develop an approach exploiting a generic 3D face model. A look-up table of facial feature correspondence between different views is applied to an input image, yielding a virtual view face. We show experimentally that the four methods developed individually outperform the classical method of Principal Component Analysis(PCA)-Linear Discriminant Analysis(LDA). Further performance gains are achieved by combining the outputs of these face recognition methods using different fusion strategies.

Keywords

Face Recognition Linear Discriminant Analysis Face Image Base Classifier Face Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tae-Kyun Kim
    • 1
  • Josef Kittler
    • 2
  1. 1.HCI LabSamsung Advanced Institute of TechnologyYonginKorea
  2. 2.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

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