Complete Gradient Face: A Novel Illumination Invariant Descriptor

In the past decade, illumination problem has been the bottleneck of developing robust face recognition systems. Extracting illumination invariant features, especially the gradient based descriptor [13], is an effective tool to solve this issue. In this paper, we propose a novel gradient based descriptor, namely Complete Gradient Face (CGF), to compensate the limitations in [13] and contribute in three folds: (1) we incorporate homogeneous filtering to alleviate the illumination effect and enhance facial information based on the Lambertian assumption; (2) we demonstrate the gradient magnitude in logarithm domain is insensitive to lighting change; (3) we propose a histogram based feature descriptor to integrate both magnitude and orientation information. Experiments on CMU-PIE and Extended YaleB are conducted to verify the effectiveness of our proposed method.