A Solution for Illumination Challenged Face Recognition using Exemplar-based Synthesis Technique

This paper addresses the problem of illumination variations affecting the performance of real world face recognition systems. We propose an exemplar (or appearance)-based technique to compute the canonical representation of the human face. The canonical image is equivalent to the reflectance field of the face that is invariant to illumination. We subsequently use the canonical face to perform the following two tasks: (1) Extract the series of independent lighting model which interacts with the canonical face to form the novel faces. (2) Synthesize novel face appearances using these lighting models. We demonstrate the ability of the approach to generate novel views of the face and show how they help improve the performance of illumination challenged face recognition problem.