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Gaussian Mixture 3D Morphable Face Model

Koppen, Paul, Feng, Zhenhua, Kittler, Josef, Awais, Muhammad, Christmas, William, Wu, Xiao-Jun and Yin, He-Feng (2017) Gaussian Mixture 3D Morphable Face Model Pattern Recognition.

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2017_GM3DMM.pdf - Accepted version Manuscript
Restricted to Repository staff only until 10 September 2018.

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3D Morphable Face Models (3DMM) have been used in pattern recognition for some time now. They have been applied as a basis for 3D face recognition, as well as in an assistive role for 2D face recognition to perform geometric and photometric normalisation of the input image, or in 2D face recognition system training. The statistical distribution underlying 3DMM is Gaussian. However, the single-Gaussian model seems at odds with reality when we consider different cohorts of data, e.g. Black and Chinese faces. Their means are clearly different. This paper introduces the Gaussian Mixture 3DMM (GM-3DMM) which models the global population as a mixture of Gaussian subpopulations, each with its own mean. The proposed GM-3DMM extends the traditional 3DMM naturally, by adopting a shared covariance structure to mitigate small sample estimation problems associated with data in high dimensional spaces. We construct a GM-3DMM, the training of which involves a multiple cohort dataset, SURREY-JNU, comprising 942 3D face scans of people with mixed backgrounds. Experiments in fitting the GM-3DMM to 2D face images to facilitate their geometric and photometric normalisation for pose and illumination invariant face recognition demonstrate the merits of the proposed mixture of Gaussians 3D face model.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Date : 9 September 2017
Identification Number : 10.1016/j.patcog.2017.09.006
Copyright Disclaimer : © 2017 Elsevier Ltd. All rights reserved.
Uncontrolled Keywords : Gaussian-mixture Model; 3D Morphable Model; 3D Face Reconstruction; Face Model Fitting; Face Recognition
Depositing User : Jane Hindle
Date Deposited : 11 Sep 2017 13:41
Last Modified : 22 Sep 2017 08:01

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