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Soft Computing

, Volume 23, Issue 1, pp 241–249 | Cite as

Sign correlation subspace for face alignment

  • Dansong Cheng
  • Yongqiang Zhang
  • Feng Tian
  • Ce Liu
  • Xiaofang Liu
Methodologies and Application
  • 50 Downloads

Abstract

Face alignment is an essential task for facial performance capture and expression analysis. Current methods such as random subspace supervised descent method, stage-wise relational dictionary and coarse-to-fine shape searching can ease multi-pose face alignment problem, but no method can deal with the multiple local minima problem directly. In this paper, we propose a sign correlation subspace method for domain partition in only one reduced low-dimensional subspace. Unlike previous methods, we analyze the sign correlation between features and shapes and project both of them into a mutual sign correlation subspace. Each pair of projected shape and feature keeps their signs consistent in each dimension of the subspace, so that each hyper octant holds the condition that one general descent exists. Then a set of general descents are learned from the samples in different hyperoctants. Requiring only the feature projection for domain partition, our proposed method is effective for face alignment. We have validated our approach with the public face datasets which include a range of poses. The validation results show that our method can reveal their latent relationships to poses. The comparison with state-of-the-art methods demonstrates that our method outperforms them, especially in uncontrolled conditions with various poses, while enjoying the comparable speed.

Keywords

Sign correlation Sparse representation Supervised descent method Face alignment 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 61402133)

Compliance with ethical standards

Conflict of interest

All authors declare that there is no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Dansong Cheng
    • 1
  • Yongqiang Zhang
    • 1
  • Feng Tian
    • 3
  • Ce Liu
    • 1
  • Xiaofang Liu
    • 2
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinPeople’s Republic of China
  2. 2.School of Electrical Engineering and AutomationHarbin Institute of TechnologyHarbinPeople’s Republic of China
  3. 3.Faculty of Science and TechnologyBournemouth UniversityPooleUK

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