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Chinese Conference on Biometric Recognition

CCBR 2017: Biometric Recognition pp 13–22Cite as

Deep Transformation Learning for Depression Diagnosis from Facial Images

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10568))

Abstract

As a severe emotional disorder, depression seriously affects people’s thoughts, behavior, feeling, sense of well-being and daily life. With the increasing number of depression patients, it has aroused the attention of researchers in this field. An effective and reliable machine learning based system has been expected to facilitate automated depression diagnose. This paper presents a novel deep transformation learning (DTL) method for visual-based depression recognition. Different from most existing depression recognition methods, our DTL trains a deep neural network that learns a set of hierarchical nonlinear transformations to project original input features into a new feature subspace, so as to capture the non-linear manifold of depression data. Extensive experiments are conducted on the AVEC2014 dataset and the results demonstrate that our method is highly competitive to several state-of-the-art methods for automated prediction of the severity of depression.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China under grant No. 61373090, the Beijing Great Scholars Program under grant No. CIT&TCD20170322, and the Youth Innovative Research Team of Capital Normal University.

Author information

Authors and Affiliations

  1. College of Information Engineering, Capital Normal University, Beijing, 100048, China

    Yajun Kang, Xiao Jiang, Ye Yin, Yuanyuan Shang & Xiuzhuang Zhou

Authors
  1. Yajun Kang
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  2. Xiao Jiang
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  3. Ye Yin
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  4. Yuanyuan Shang
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  5. Xiuzhuang Zhou
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Corresponding author

Correspondence to Xiuzhuang Zhou .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Computing and Technology, Chinese Academy of Sciences, Beijing, China

    Yong Xu

  5. Shenzhen University, Shenzhen, China

    Linlin Shen

  6. Tsinghua University, Beijing, China

    Jianjiang Feng

  7. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  8. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yu Qiao

  9. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Zhenhua Guo

  10. Shenzhen University, Shenzhen, China

    Shiqi Yu

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© 2017 Springer International Publishing AG

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Cite this paper

Kang, Y., Jiang, X., Yin, Y., Shang, Y., Zhou, X. (2017). Deep Transformation Learning for Depression Diagnosis from Facial Images. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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