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Two-Layer Feature Selection Algorithm for Recognizing Human Emotions from 3D Motion Analysis

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Advances in Computer Graphics (CGI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11542))

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Abstract

Research on automatic recognition of human emotion from motion is gaining momentum, especially in the areas of virtual reality, robotics, behavior modeling, and biometric identity recognition. One of the challenges is to identify emotion-specific features from a vast number of expressive descriptors of human motion. In this paper, we have developed a novel framework for emotion classification using motion features. We combined a filter-based feature selection algorithm and a genetic algorithm to recognize four basic emotions: happiness, sadness, fear, and anger. The validity of the proposed framework was confirmed on a dataset containing 30 subjects performing expressive walking sequences. Our proposed framework achieved a very high recognition rate outperforming existing state-of-the-art methods in the literature.

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Acknowledgements

Authors would like to acknowledge partial support from NSERC DG “Machine Intelligence for Biometric Security”, NSERC ENGAGE on Gait Recognition and NSERC SPG on Smart Cities funding.

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Authors and Affiliations

  1. University of Calgary, Calgary, AB, Canada

    Ferdous Ahmed & Marina L. Gavrilova

Authors
  1. Ferdous Ahmed
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  2. Marina L. Gavrilova
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Corresponding author

Correspondence to Ferdous Ahmed .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

  2. Bournemouth University, Poole, UK

    Jian Chang

  3. University of Geneva, MIRALab, Carouge, Switzerland

    Nadia Magnenat Thalmann

  4. International Christian University, Tokyo, Japan

    Eckhard Hitzer

  5. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

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Ahmed, F., Gavrilova, M.L. (2019). Two-Layer Feature Selection Algorithm for Recognizing Human Emotions from 3D Motion Analysis. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-22514-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22513-1

  • Online ISBN: 978-3-030-22514-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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