Abstract
Lighting estimation from faces 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 noise. To alleviate the effect of such noise, we utilize
the idea of Generative Adversarial Networks (GAN) and
propose a Label Denoising Adversarial Network (LDAN).
LDAN makes 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. To further evaluate the proposed method, we also
apply it to regress object 2D key points where ground truth
labels are available. Our experiments demonstrate its effectiveness on this application