Coupled Marginal Fisher Analysis for Low-Resolution Face Recognition

  • Stephen Siena
  • Vishnu Naresh Boddeti
  • B. V. K. Vijaya Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7584)


Many scenarios require that face recognition be performed at conditions that are not optimal. Traditional face recognition algorithms are not best suited for matching images captured at a low-resolution to a set of high-resolution gallery images. To perform matching between images of different resolutions, this work proposes a method of learning two sets of projections, one for high-resolution images and one for low-resolution images, based on local relationships in the data. Subsequent matching is done in a common subspace. Experiments show that our algorithm yields higher recognition rates than other similar methods.


Face Recognition Linear Discriminant Analysis Recognition Rate Local Binary Pattern Probe Image 
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 2012

Authors and Affiliations

  • Stephen Siena
    • 1
  • Vishnu Naresh Boddeti
    • 1
  • B. V. K. Vijaya Kumar
    • 1
  1. 1.Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA

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