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The Euro-China Conference on Intelligent Data Analysis and Applications

ECC 2018: Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications pp 717–725Cite as

Face Alignment Based on K-Means

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Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Author information

Authors and Affiliations

  1. Xi’an Polytechnic University, Xi’an, 710048, China

    Yunhong Li & Qiaoning Yuan

Authors
  1. Yunhong Li
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  2. Qiaoning Yuan
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Corresponding author

Correspondence to Yunhong Li .

Editor information

Editors and Affiliations

  1. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Pavel Krömer

  2. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, China

    Hong Zhang

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

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Cite this paper

Li, Y., Yuan, Q. (2019). Face Alignment Based on K-Means. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_81

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