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Multimedia Tools and Applications

, Volume 78, Issue 3, pp 3221–3238 | Cite as

Robust facial landmark extraction scheme using multiple convolutional neural networks

  • Hyungjoon Kim
  • Jisoo Park
  • HyeonWoo Kim
  • Eenjun HwangEmail author
  • Seungmin Rho
Article
  • 313 Downloads

Abstract

Facial landmarks are a set of features that can be distinguished on the human face with the naked eye. Typical facial landmarks include eyes, eyebrows, nose, and mouth. Landmarks play an important role in human-related image analysis. For example, they can be used to determine whether there is a human being in the image, identify who the person is, or recognize the orientation of a face when taking a photograph. General techniques for detecting facial landmarks can be classified into two groups: One is based on traditional image processing techniques, such as Haar cascade classifiers and edge detection. The other is based on machine learning techniques in which landmarks can be detected by training neural network using facial features. However, such techniques have shown low accuracy, especially in some special conditions such as low luminance and overlapped faces. To overcome these problems, we proposed in our previous work a facial landmark extraction scheme using deep learning and semantic segmentation, and demonstrated that with even a small dataset, our scheme could achieve reasonable facial landmark extraction performance under such conditions. Nevertheless, for more extensive dataset, we found several exceptional cases where the scheme could not detect face landmarks precisely. Hence, in this paper, we revise our facial landmark extraction scheme using a deep learning model called Faster R-CNN and show how our scheme can improve the overall performance by handling such exceptional cases appropriately. Also, we show how to expand the training dataset by using image filters and image operations such as rotation for more robust landmark detection.

Keywords

Convolutional neural networks Facial landmark Semantic segmentation Object detection Faster R-CNN 

Notes

Acknowledgements

This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Public Technology Program based on Environmental Policy, funded by Korea Ministry of Environment (MOE)(2017000210001).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018
corrected publication September/2018

Authors and Affiliations

  • Hyungjoon Kim
    • 1
  • Jisoo Park
    • 1
  • HyeonWoo Kim
    • 1
  • Eenjun Hwang
    • 1
    Email author
  • Seungmin Rho
    • 2
  1. 1.School of Electrical Engineering, Korea UniversitySeoulRepublic of Korea
  2. 2.Department of Media SoftwareSungkyul UniversityAnyangRepublic of Korea

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