@conference { , title = {BRDF estimation for faces from a sparse dataset using a neural network}, abstract = {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be the most photo-realistic.}, conference = {Computer Analysis of Images and Patterns, CAIP 2013}, publicationstatus = {Unpublished}, url = {https://uwe-repository.worktribe.com/output/928903}, keyword = {Centre for Machine Vision, Bristol Robotics Laboratory, BDRF, neural network}, year = {2013}, author = {Hansen, Mark F and Atkinson, Gary and Smith, Melvyn} }