文摘
Describes a conceptual framework for 3D-2D (or 2D-3D) face recognition. Proposes a novel 3D-2D system for 2D image face recognition from 3D datasets. Proposes a method to build subject-specific 3D gallery models, using 3D+2D data, and a method for model-based, texture representation and relighting. 3D-2D recognition surpasses 2D-2D on challenging 2D+3D data with pose and illumination variations, and can approximate 3D-3D, shape-based similarity methods. Representation and normalization using 3D models can compensate for non-frontal poses or different lighting conditions.