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Facial Landmark Localization by Part-Aware Deep Convolutional Network

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Advances in Multimedia Information Processing - PCM 2016 (PCM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9916))

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Abstract

Facial landmark localization is a very challenging research task. The localization accuracy of landmarks on separate facial parts differ greatly due to texture and shape, however most existing methods fail to consider the part location of landmarks. To solve this problem, we propose a novel end-to-end regression framework using deep convolutional neural network (CNN). Our deep architecture first encodes the image into feature maps shared by all the landmarks. Then, these features are sent into two independent sub-network modules to regress contour landmarks and inner landmarks, respectively. Extensive evaluations conducted on 300-W benchmark dataset demonstrate the proposed deep framework achieves state-of-the-art results.

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Acknowledgments

This work was supported in part by two STCSM’s Programs. (No. 15511104402 & 15JC1400103)

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Authors and Affiliations

  1. Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

    Keke He & Xiangyang Xue

Authors
  1. Keke He
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  2. Xiangyang Xue
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Corresponding author

Correspondence to Xiangyang Xue .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, China

    Enqing Chen

  2. Jiaotong University, Xi’an, China

    Yihong Gong

  3. Zhengzhou University, Zhengzhou, China

    Yun Tie

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He, K., Xue, X. (2016). Facial Landmark Localization by Part-Aware Deep Convolutional Network. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-48890-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48889-9

  • Online ISBN: 978-3-319-48890-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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