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Sign-Correlation Partition Based on Global Supervised Descent Method for Face Alignment

  • Yongqiang ZhangEmail author
  • Shuang Liu
  • Xiaosong Yang
  • Daming Shi
  • Jian Jun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)

Abstract

Face alignment is an essential task for facial performance capture and expression analysis. As a complex nonlinear problem in computer vision, face alignment across poses is still not studied well. Although the state-of-the-art Supervised Descent Method (SDM) has shown good performance, it learns conflict descent direction in the whole complex space due to various poses and expressions. Global SDM has been presented to deal with this case by domain partition in feature and shape PCA spaces for face tracking and pose estimation. However, it is not suitable for the face alignment problem due to unknown ground truth shapes. In this paper we propose a sign-correlation subspace method for the domain partition of global SDM. In our method only one reduced low dimensional subspace is enough for domain partition, thus adjusting the global SDM efficiently for face alignment. 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 keep sign consistent in each dimension of the subspace, so that each hyperoctant holds the condition that one general descent exists. Then a set of general descent directions are learned from the samples in different hyperoctants. Our sign-correlation partition method is validated in the public face datasets, which includes a range of poses. It indicates that our methods can reveal their latent relationships to poses. The comparison with state-of-the-art methods for face alignment demonstrates that our method outperforms them especially in uncontrolled conditions with various poses, while keeping comparable speed.

Keywords

Face Image Shape Space Active Appearance Model Active Shape Model Face Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by Harbin Institute of Technology Scholarship Fund 2016 and National Centre for Computer Animation, Bournemouth University.

Supplementary material

416261_1_En_19_MOESM1_ESM.zip (19 kb)
Supplementary material 1 (zip 18 KB)
416261_1_En_19_MOESM2_ESM.zip (2.2 mb)
Supplementary material 2 (zip 2296 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yongqiang Zhang
    • 1
    Email author
  • Shuang Liu
    • 2
  • Xiaosong Yang
    • 2
  • Daming Shi
    • 1
  • Jian Jun Zhang
    • 2
  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.Bournemouth UniversityPooleUK

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