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We present a new method for face recognition from digital images acquiredunder varying illumination conditions. The method is based on mathematicalmodeling of local gradient distributions using the Radon CumulativeDistribution Transform (R-CDT). We demonstrate that lighting variations causecertain types of deformations of local image gradient distributions which, whenexpressed in R-CDT domain, can be modeled as a subspace. Face recognition isthen performed using a nearest subspace in R-CDT domain of local gradientdistributions. Experiment results demonstrate the proposed method outperformsother alternatives in several face recognition tasks with challengingillumination conditions. Python code implementing the proposed method isavailable, which is integrated as a part of the software package PyTransKit.

Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face Recognition