SFS based view synthesis for robust face recognition

Abstract

Sensitivity to variations in pose is a challenging problem in face recognition using appearance-based methods. More specifically, the appearance of a face changes dramatically when viewing and/or lighting directions change. Various approaches have been proposed to solve this difficult problem. They can be broadly divided into three classes: (1) multiple image-based methods where multiple images of various poses per person are available; (2) hybrid methods where multiple example images are available during learning but only one database image per person is available during recognition; and (3) single image-based methods where no example-based learning is carried out. We present a method that comes under class 3. This method, based on shape-from-shading (SFS), improves the performance of a face recognition system in handling variations due to pose and illumination via image synthesis.