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Motion Attention Deep Transfer Network for Cross-database Micro-expression Recognition

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

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Abstract

Cross-database micro-expression recognition is a great challenging problem due to the short duration and low intensity of micro-expressions from different collection conditions. In this paper, we present a Motion Attention Deep Transfer Network (MADTN) that can focus on the most discriminative movement regions of the face and reduce the database bias. Specifically, we firstly combine the motion information and facial appearance information to obtain the discriminative representation by merging the optical flow fields between three key-frames (the onset frame, the middle frame, the offset frame) and the facial appearance of the middle frame. Then, the deep network architecture extracts cross-domain feature with the superiority of the maximum mean discrepancy(MMD) loss so that the source and target domains have a similar distribution. Results on benchmark cross-database micro-expression experiments demonstrate that the MADTN achieves remarkable performance in many micro-expression transfer tasks and exceed the state-of-the-art results, which show the robustness and superiority of our approach.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1305200, in part by the National Natural Science Foundation of China under Grant 61921004, Grant 61902064, Grant 81971282, Grant U2003207, and Grant 62076064, and in part by the Fundamental Research Funds for the Central Universities under Grant 2242018K3DN01.

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Southeast University, Nanjing, 210096, China

    Wanchuang Xia

  2. School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China

    Wenming Zheng, Yuan Zong & Xingxun Jiang

Authors
  1. Wanchuang Xia
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  2. Wenming Zheng
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  3. Yuan Zong
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  4. Xingxun Jiang
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Corresponding author

Correspondence to Wenming Zheng .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Xia, W., Zheng, W., Zong, Y., Jiang, X. (2021). Motion Attention Deep Transfer Network for Cross-database Micro-expression Recognition. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_49

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  • DOI: https://doi.org/10.1007/978-3-030-68796-0_49

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