Mixture of Classifiers for Face Recognition across Pose

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish (US)
Title of host publicationCross Disciplinary Biometric Systems
EditorsChengjun Liu, Vijay Kumar Mago
Number of pages20
StatePublished - 2012

Publication series

NameIntelligent Systems Reference Library
ISSN (Print)1868-4394
ISSN (Electronic)1868-4408

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Information Systems and Management
  • Library and Information Sciences


Dive into the research topics of 'Mixture of Classifiers for Face Recognition across Pose'. Together they form a unique fingerprint.

Cite this