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Joint Face Alignment: Rescue Bad Alignments with Good Ones by Regularized Re-fitting

  • Xiaowei Zhao
  • Xiujuan Chai
  • Shiguang Shan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)

Abstract

Nowadays, more and more applications need to jointly align a set of facial images from one specific person, which forms the so-called joint face alignment problem. To address this problem, in this paper, starting from an initial face alignment results, we propose to enhance the alignments by a fundamentally novel idea: rescuing the bad alignments with their well-aligned neighbors. In our method, a discriminative alignment evaluator is well designed to assess the initial face alignments and separate the well-aligned images from the badly-aligned ones. To correct the bad ones, a robust regularized re-fitting algorithm is proposed by exploiting the appearance consistency between the badly-aligned image and its k well-aligned nearest neighbors. Experiments conducted on faces in the wild demonstrate that our method greatly improves the initial face alignment results of an off-the-shelf facial landmark locator. In addition, the effectiveness of our method is validated through comparing with other state-of-the-art methods in joint face alignment under complex conditions.

Keywords

Facial Image Appearance Model Alignment Result Active Appearance Model Active Shape Model 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaowei Zhao
    • 1
    • 2
  • Xiujuan Chai
    • 1
  • Shiguang Shan
    • 1
  1. 1.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology, CASBeijingChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina

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