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Lighting estimation from face images is an important task and hasapplications in many areas such as image editing, intrinsic imagedecomposition, and image forgery detection. We propose to train a deepConvolutional Neural Network (CNN) to regress lighting parameters from a singleface image. Lacking massive ground truth lighting labels for face images in thewild, we use an existing method to estimate lighting parameters, which aretreated as ground truth with unknown noises. To alleviate the effect of suchnoises, we utilize the idea of Generative Adversarial Networks (GAN) andpropose a Label Denoising Adversarial Network (LDAN) to make use of syntheticdata with accurate ground truth to help train a deep CNN for lightingregression on real face images. Experiments show that our network outperformsexisting methods in producing consistent lighting parameters of different facesunder similar lighting conditions. Moreover, our method is 100,000 times fasterin execution time than prior optimization-based lighting estimation approaches.

Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images