Face Recognition Using a Unified 3D Morphable Model

Guosheng Hu, Fei Yan, Chi-Ho Chan, Weihong Deng, William Christmas, Josef Kittler, Neil M. Robertson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)
1997 Downloads (Pure)


We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII
PublisherSpringer Verlag
Number of pages17
ISBN (Electronic)978-3-319-46484-8
ISBN (Print)978-3-319-46483-1
Publication statusPublished - 17 Sep 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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