An Optimized Illumination Normalization Method for Face Recognition
Differences in illumination conditions cause significant challenges for any 2-D face recognition algorithm. One of the methods to counter these effects is image preprocessing before feature extraction. In this paper we present a new preprocessing approach that uses custom filters obtained through an optimization procedure striving for most suitable preprocessing filters for the selected feature extractor and distance measure. We experiment with it using Local Binary Pattern texture features and X2 histogram distance metric. Results are provided for Face Recognition Grand Challenge (FRGC) 1.0.4 dataset.