We present a novel �ve source near-infrared photometric
stereo 3D face capture device. The accuracy of the system is demonstrated by a comparison with ground truth from a commercial 3D scanner. We also use the data from the �ve captured images to model the Bi-directional Reectance Distribution Function (BRDF) in order to synthesise images from novel lighting directions. A comparison of these synthetic images created from modelling the BRDF using a three layer neural network, a linear interpolation method and the Lambertian model is given, which shows that the neural network proves to be the most photo-realistic.
Hansen, M. F., Atkinson, G., & Smith, M. (2013, August). BRDF estimation for faces from a sparse dataset using a neural network. Paper presented at Computer Analysis of Images and Patterns, CAIP 2013