Mark Hansen Mark.Hansen@uwe.ac.uk
Professor of Machine Vision and Machine Learning
BRDF estimation for faces from a sparse dataset using a neural network
Hansen, Mark F; Atkinson, Gary; Smith, Melvyn
Authors
Gary Atkinson Gary.Atkinson@uwe.ac.uk
Associate Professor
Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof
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.
| Presentation Conference Type | Conference Paper (unpublished) |
|---|---|
| Conference Name | Computer Analysis of Images and Patterns, CAIP 2013 |
| Start Date | Aug 27, 2013 |
| End Date | Aug 29, 2013 |
| Publication Date | Aug 27, 2013 |
| Publicly Available Date | Jun 7, 2019 |
| Peer Reviewed | Peer Reviewed |
| Keywords | BDRF, neural network |
| Public URL | https://uwe-repository.worktribe.com/output/928903 |
| Publisher URL | http://www.cs.york.ac.uk/cvpr/caip2013/Program.php |
| Additional Information | Title of Conference or Conference Proceedings : Computer Analysis of Images and Patterns, CAIP 2013 |
Files
Mark F. Hansen, G. A. Atkinson and M. L. Smith. BRDF estimation for faces from a sparse dataset using a neural network. In Computer Analysis of Images and Patterns.pdf
(2.5 Mb)
PDF
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