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Sign-correlation cascaded regression for face alignment

  • Dansong Cheng
  • Yongqiang Zhang
  • Ce Liu
  • Xiaofang LiuEmail author
Article
  • 3 Downloads

Abstract

Face alignment plays an important role in many applications such as face recognition and face reconstruction. Current regression based approaches can ease the multi-pose face alignment problem, but they fail to deal with the multiple local minima problem directly. To improve the performance of multi-pose facial landmark localization, in this paper we propose a sign correlation supervised descent method (SC-SDM) based on a nonlinear optimization theory. SC-SDM analyses the sign correlation between features and shapes and project both of them into a mutual sign-correlation subspace. By partitioning the whole multi-pose samples into a series of pose-consistent subsets, a group of models are learned from each subset. The experiments using the public multi-pose datasets has validated the partition and proved that SC-SDM can accurately separate samples into pose-consistent subsets, which reveals their latent relationships to pose. The comparison with state-of-the-art methods demonstrates that SC-SDM outperforms them, especially in uncontrolled conditions with various poses.

Keywords

Sign correlation Supervised descent method Face alignment cascaded regression 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Dansong Cheng
    • 1
  • Yongqiang Zhang
    • 1
  • Ce Liu
    • 1
  • Xiaofang Liu
    • 2
    Email author
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.School of Electrical Engineering and AutomationHarbin Institute of TechnologyHarbinChina

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