  Lighting estimation from face images is an important task and has
applications in many areas such as image editing, intrinsic image
decomposition, and image forgery detection. We propose to train a deep
Convolutional Neural Network (CNN) to regress lighting parameters from a single
face image. Lacking massive ground truth lighting labels for face images in the
wild, we use an existing method to estimate lighting parameters, which are
treated as ground truth with unknown noises. To alleviate the effect of such
noises, we utilize the idea of Generative Adversarial Networks (GAN) and
propose a Label Denoising Adversarial Network (LDAN) to make use of synthetic
data with accurate ground truth to help train a deep CNN for lighting
regression on real face images. Experiments show that our network outperforms
existing methods in producing consistent lighting parameters of different faces
under similar lighting conditions. Moreover, our method is 100,000 times faster
in execution time than prior optimization-based lighting estimation approaches.
