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

Hansen, Mark F; Atkinson, Gary; Smith, Melvyn

Authors

Mark Hansen Mark.Hansen@uwe.ac.uk
Associate Professor in Knowledge Exchange & External Engagement

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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.

Citation

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

Presentation Conference Type Conference Paper (unpublished)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
Peer Reviewed Peer Reviewed
Keywords BDRF, neural network
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)
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