Multidimensional Systems and Signal Processing

, Volume 21, Issue 3, pp 213–229 | Cite as

Perfect histogram matching PCA for face recognition

  • Ana-Maria Sevcenco
  • Wu-Sheng Lu


We present an enhanced principal component analysis (PCA) algorithm for improving rate of face recognition. The proposed pre-processing method, termed as perfect histogram matching, modifies the image histogram to match a Gaussian shaped tonal distribution in the face images such that spatially the entire set of face images presents similar facial gray-level intensities while the face content in the frequency domain remains mostly unaltered. Computationally inexpensive, the perfect histogram matching algorithm proves to yield superior results when applied as a pre-processing module prior to the conventional PCA algorithm for face recognition. Experimental results are presented to demonstrate effectiveness of the technique.


Principal component analysis Histogram matching Face recognition 


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of VictoriaVictoriaCanada

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