Learning Face Appearance under Different Lighting Conditions

We propose a machine learning approach for estimating intrinsic faces and hence de-illuminating and re-illuminating faces directly in the image domain. The most challenging step is de-illumination, where unlike existing methods that require either the 3D geometry or expensive setups, we show that the problem can be solved with relatively simple kernel regression models. For this purpose, the problem of decomposing an observed image into its intrinsic components, i.e. reflectance and albedo, is formulated as a nonlinear regression problem. The estimation of an intrinsic component is then accomplished by estimating local linear constraints on images in terms of derivatives using multi-scale patches of the observed images, comprising from a three-level Laplacian Pyramid. We have evaluated our method on "Extended Yale Face Database B" and shown that despite its simplicity, the method is able to produce realistic results using images taken from only four different lighting orientations.