Illuminated facial preprocessing with Wavelet Local Binary Patterns Histogram Specification

Contemporary 2D face recognition is still a challenging work, especially when lighting varies. Thus, many works of resolving illumination variation in face recognition have been proposed, in the past decades. In this paper, we proposed Wavelet Local Binary Patterns Histogram Specification as a preprocessing technique for illuminated face recognition. Based on wavelet analysis, an illuminated facial image is decomposed into illumination and reflectance components. The illumination component that resides in the low spatial-frequency subband is first removed. Next, the reflectance component that resides in the high and middle spatial-frequency subband is then enhanced with local binary pattern histogram. This technique is promising in achieving better recognition performance on YaleB and CMU PIE face databases in comparison to the results that achieved by existing illumination invariant techniques.

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