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Enhancing 3D Facial Expression Recognition by Exaggerating Geometry Characteristics

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Biometric Recognition (CCBR 2017)

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

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Abstract

This paper studies exaggerated facial shapes in addition to original facial shapes to assist 3D Facial Expression Recognition (FER). We propose a Poisson equation based approach to exaggerate facial shape characteristics to highlight expression clues that are latent in original facial surfaces but useful for recognizing expressions. To validate this idea, we exploit two off-the-shelf descriptors that reach state of the art performance in 3D FER, namely Geometric Scattering Representation (GSR) and Multi-Scale Local Normal Patterns (MS-LNPs) for expression-related feature extraction, and adopt early fusion to combine the credits of the original surface and the enhanced one, followed by the SVMs and Multiple Kernel Learning (MKL) classifiers. The accuracy gain of two features achieved on BU-3DFE is 0.8% and 1.3% respectively. Such results show that the exaggerated faces are complementary to the original faces in discriminating different facial expressions in the 3D domain.

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Acknowledgement

This work is partly supported by the National Natural Science Foundation of China (No. 61673033); the Research Program of State Key Laboratory of Software Development Environment (SKLSDE-2017ZX-07); and Microsoft Research Asia Collaborative Program (FY17-RES-THEME-033).

Author information

Authors and Affiliations

  1. IRIP Lab, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Weijian Li, Yunhong Wang & Di Huang

  2. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, China

    Huibin Li

Authors
  1. Weijian Li
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  2. Yunhong Wang
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  3. Huibin Li
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  4. Di Huang
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Corresponding author

Correspondence to Di Huang .

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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Li, W., Wang, Y., Li, H., Huang, D. (2017). Enhancing 3D Facial Expression Recognition by Exaggerating Geometry Characteristics. 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_21

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

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