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Pacific Rim Conference on Multimedia

PCM 2017: Advances in Multimedia Information Processing – PCM 2017 pp 184–193Cite as

Face Alignment Using Local Probabilistic Features

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10735))

Abstract

Random forest is an effective tool for locating facial landmarks. In this paper, we propose a novel random forest based face alignment method using local probabilistic features (LPF). Here, the LPF has the property of calculating the probability of a sample belonging to the leaf nodes of a tree. The obtained LPF is then used to train a regression model for approximating to the real location of facial landmarks. The above procedure is repeated several times step-by-step in a cascaded form until the model converges. In the end, various convergent results are combined to overcome the instability of a single one. By this way, our method markedly outperforms the state-of-the-art methods. Experimental results show the effectiveness of our algorithm on various face alignment datasets.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61472393, No. 61572450 and No. 61303150), the Fundamental Research Funds for the Central Universities (WK2350000002).

Author information

Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, China

    Qing Lu, Jun Yu & Zengfu Wang

  2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China

    Zengfu Wang

Authors
  1. Qing Lu
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  2. Jun Yu
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  3. Zengfu Wang
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Corresponding author

Correspondence to Zengfu Wang .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

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Lu, Q., Yu, J., Wang, Z. (2018). Face Alignment Using Local Probabilistic Features. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-77380-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77379-7

  • Online ISBN: 978-3-319-77380-3

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