BRDF estimation for faces from a sparse dataset using a neural network

Hansen, M. F., Atkinson, G. and Smith, M. (2013) BRDF estimation for faces from a sparse dataset using a neural network. In: Computer Analysis of Images and Patterns, CAIP 2013, York, UK, 27-29th August, 2013. Available from: http://eprints.uwe.ac.uk/20815

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Publisher's URL: http://www.cs.york.ac.uk/cvpr/caip2013/Program.php

Abstract/Description

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:BDRF, neural network
Faculty/Department:Faculty of Environment and Technology > Department of Engineering Design and Mathematics
ID Code:20815
Deposited By: Professor M. Smith
Deposited On:31 Jul 2013 09:12
Last Modified:03 Sep 2016 00:19

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