  We present a new method for face recognition from digital images acquired
under varying illumination conditions. The method is based on mathematical
modeling of local gradient distributions using the Radon Cumulative
Distribution Transform (R-CDT). We demonstrate that lighting variations cause
certain types of deformations of local image gradient distributions which, when
expressed in R-CDT domain, can be modeled as a subspace. Face recognition is
then performed using a nearest subspace in R-CDT domain of local gradient
distributions. Experiment results demonstrate the proposed method outperforms
other alternatives in several face recognition tasks with challenging
illumination conditions. Python code implementing the proposed method is
available, which is integrated as a part of the software package PyTransKit.
