A Practical Face Relighting Method for Directional Lighting Normalization

Abstract

We propose a simplified and practical computational technique for estimating directional lighting in uncalibrated images of faces in frontal pose. We show that this inverse problem can be solved using constrained least-squares and class-specific priors on shape and reflectance. For simplicity, the principal illuminant is modeled as a mixture of Lambertian and ambient components. By using a generic 3D face shape and an average 2D albedo we can efficiently compute the directional lighting with surprising accuracy (in real-time and with or without shadows). We then use our lighting direction estimate in a forward rendering step to “relight” arbitrarily-lit input faces to a canonical (diffuse) form as needed for illumination-invariant face verification. Experimental results with the Yale Face Database B as well as real access-control datasets illustrate the advantages over existing pre-processing techniques such as a linear ramp (facet) model commonly used for lighting normalization.