Robust Face Recognition under Difficult Lighting Conditions

? This paper addresses the problem of illumination effects on Face recognition and works for an approach to reduce their effect on recognition performance. More broadly, a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition. Using local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions. We also show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and Robustness is still improved by adding Kernel principal component analysis (PCA) feature extraction.