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Mixture of Classifiers for Face Recognition across Pose

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Cross Disciplinary Biometric Systems

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

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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.

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Authors and Affiliations

  1. New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Chengjun Liu

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  1. Chengjun Liu
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Correspondence to Chengjun Liu .

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© 2012 Springer Berlin Heidelberg

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Liu, C. (2012). Mixture of Classifiers for Face Recognition across Pose. In: Cross Disciplinary Biometric Systems. Intelligent Systems Reference Library, vol 37. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28457-1_5

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  • DOI: https://doi.org/10.1007/978-3-642-28457-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28456-4

  • Online ISBN: 978-3-642-28457-1

  • eBook Packages: EngineeringEngineering (R0)

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