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Face Alignment Based on K-Means

  • Yunhong LiEmail author
  • Qiaoning Yuan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

In order to address the generalization problem when Active Appearance Model (AAM) is applied to unseen subjects and images. In this paper, an accurate face alignment algorithm based on K-means is proposed to tackle the generalization problem of AAM. Firstly, the original AAM is reformulated as a sparsity-regularized problem. Then, for an input facial image, we learn a strong localized shape and appearance prior through exploiting its K-similar patterns to further approximate sparse representation problem. Finally, learning many localized linear face model instead of a global non-linear face model. Through numerical experiments, this approach is shown to outperform some common existing methods on the task of generic face fitting.

Keywords

Face alignment K-means K-nearest neighbors 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Xi’an Polytechnic UniversityXi’anChina

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