Mixture of Classifiers for Face Recognition across Pose

Part of the Intelligent Systems Reference Library book series (ISRL, volume 37)

Abstract

A two dimensional Mixture of Classifiers (MoC) method is presented in this chapter for face recognition across pose. The 2D MoC method performs first pose classification with predefined pose categories and then face recognition within each individual pose class. The main contributions of the paper come from (i) proposing an effective pose classification method by addressing the scales problem of images in different pose classes, and (ii) applying pose-specific classifiers for face recognition. Comparing with the 3D methods for face recognition across pose, the 2D MoC method does not require a large number of manual annotations or a complex and expensive procedure of 3D modeling and fitting. Experimental results using a data set from the CMU PIE database show the feasibility of the 2D MoC method.

Keywords

Facial Expression Face Recognition Face Image Gallery Image Neutral Facial Expression 
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 Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.New Jersey Institute of TechnologyNewarkUSA

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