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Gait Emotion Recognition Using a Bi-modal Deep Neural Network

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Advances in Visual Computing (ISVC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13598))

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Abstract

Gait Emotion Recognition is an emerging research domain that focuses on the automatic detection of emotions from a person’s manner of walking. Deep learning-based methodologies have been proven highly effective for computer vision tasks. This paper provides a powerful deep-learning architecture for emotion recognition from gait by introducing the fusion of domain-specific discriminative features with latent deep features. The proposed Bi-Modal Deep Neural Network (BMDNN) combines salient features extracted from a deep neural network with highly-discriminating handcrafted features. The proposed architecture outperforms state-of-the-art methods in all emotional classes on the Edinburgh Locomotion MoCap Dataset.

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Acknowledgment

The authors acknowledge the Natural Sciences and Engineering Research Council (NSERC) Discovery Grant funding, as well as the NSERC Strategic Partnership Grant (SPG) and the Innovation for Defense Excellence and Security Network (IDEaS) for the partial funding of this project.

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Authors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, Canada

    Yajurv Bhatia, A. S. M. Hossain Bari & Marina Gavrilova

Authors
  1. Yajurv Bhatia
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  2. A. S. M. Hossain Bari
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  3. Marina Gavrilova
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Corresponding author

Correspondence to Yajurv Bhatia .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Illinois Urbana-Champaign, Urbana, IL, USA

    Bo Li

  3. National University of Singapore, Singapore, Singapore

    Angela Yao

  4. Microsoft Research Asia, Beijing, China

    Yang Liu

  5. University of Missouri, Columbia, MO, USA

    Ye Duan

  6. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  7. Idaho National Laboratory, Idaho Falls, ID, USA

    Rajiv Khadka

  8. Salesforce, Seattle, WA, USA

    Ana Crisan

  9. Tufts University, Medford, MA, USA

    Remco Chang

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Bhatia, Y., Bari, A.S.M.H., Gavrilova, M. (2022). Gait Emotion Recognition Using a Bi-modal Deep Neural Network. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13598. Springer, Cham. https://doi.org/10.1007/978-3-031-20713-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-20713-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20712-9

  • Online ISBN: 978-3-031-20713-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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