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Fitting 3D Morphable Models using Local Features

Huber, P, Feng, Z, Christmas, WJ, Kittler, J and Raetsch, M (2015) Fitting 3D Morphable Models using Local Features In: ICIP 2015, 2015-09-27 - 2015-09-30, Quebec City, Canada.

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In this paper, we propose a novel fitting method that uses local image features to fit a 3D Morphable Face Model to 2D images. To overcome the obstacle of optimising a cost function that contains a non-differentiable feature extraction operator, we use a learning-based cascaded regression method that learns the gradient direction from data. The method allows to simultaneously solve for shape and pose parameters. Our method is thoroughly evaluated on Morphable Model generated data and first results on real data are presented. Compared to traditional fitting methods, which use simple raw features like pixel colour or edge maps, local features have been shown to be much more robust against variations in imaging conditions. Our approach is unique in that we are the first to use local features to fit a 3D Morphable Model. Because of the speed of our method, it is applicable for real-time applications. Our cascaded regression framework is available as an open source library at

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Vision, Speech and Signal Processing
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Huber, P
Feng, Z
Christmas, WJ
Kittler, J
Raetsch, M
Date : September 2015
DOI : 10.1109/ICIP.2015.7350989
Copyright Disclaimer : © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 08 Mar 2016 17:26
Last Modified : 31 Oct 2017 17:28

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