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Face Recognition Using a Unified 3D Morphable Model

  • Guosheng Hu
  • Fei Yan
  • Chi-Ho Chan
  • Weihong Deng
  • William Christmas
  • Josef Kittler
  • Neil M. Robertson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9912)

Abstract

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.

Keywords

3D morphable model Face recognition 

Notes

Acknowledgments

This work was sponsored by EPSRC project ‘Signal processing in a networked battlespace’ under contract EP/K014307/1, ‘FACER2VM’ under EP/N007743/1, NSFC project under Grant 61375031 and 61573068. The support from EPSRC and the MOD University Defence Research Collaboration (UDRC) in Signal Processing is gratefully acknowledged.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Guosheng Hu
    • 1
    • 2
  • Fei Yan
    • 2
  • Chi-Ho Chan
    • 2
  • Weihong Deng
    • 3
  • William Christmas
    • 2
  • Josef Kittler
    • 2
  • Neil M. Robertson
    • 1
    • 4
  1. 1.AnyvisionBelfastUK
  2. 2.CVSSPUniversity of SurreyGuildfordUK
  3. 3.Beijing University of Posts and TelecommunicationsBeijingChina
  4. 4.ECITQueen’s University of BelfastBelfastUK

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