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Perfect histogram matching PCA for face recognition

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Abstract

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.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Victoria, 3800 Finnerty Rd., Victoria, BC, V8P 5C2, Canada

    Ana-Maria Sevcenco & Wu-Sheng Lu

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  1. Ana-Maria Sevcenco
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  2. Wu-Sheng Lu
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Correspondence to Wu-Sheng Lu.

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Sevcenco, AM., Lu, WS. Perfect histogram matching PCA for face recognition. Multidim Syst Sign Process 21, 213–229 (2010). https://doi.org/10.1007/s11045-009-0099-y

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  • DOI: https://doi.org/10.1007/s11045-009-0099-y

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