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Expressive motions recognition and analysis with learning and statistical methods

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Abstract

This paper proposes to recognize and analyze expressive gestures using a descriptive motion language, the Laban Movement Analysis (LMA) method. We extract body features based on LMA factors which describe both quantitative and qualitative aspects of human movement. In the direction of our study, a dataset of 5 gestures performed with 4 emotions is created using the motion capture Xsens. We used two different approaches for emotions analysis and recognition. The first one is based on a machine learning method, the Random Decision Forest. The second approach is based on the human’s perception. We derive the most important features for each expressed emotion using the same methods, the RDF and the human’s ratings. We compared the results obtained from the automatic learning method against human perception in the discussion section.

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Acknowledgements

We would like to thank the staff of the University of Evry Val d’Essonne for participating in our dataset. As well, we would like to thank Mrs. Alice Jourlin for help with data gathering and tabulation. This work was partially supported by the Strategic Research Initiatives project iCODE accredited by University Paris Saclay.

Funding

This study was funded by the Strategic Research Initiatives project iCODE, University Paris Saclay.

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Authors and Affiliations

  1. IBISC, Univ Evry, Université Paris-Saclay, 91025, Evry, France

    Insaf Ajili, Zahra Ramezanpanah, Malik Mallem & Jean-Yves Didier

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  1. Insaf Ajili
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  2. Zahra Ramezanpanah
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  3. Malik Mallem
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  4. Jean-Yves Didier
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Correspondence to Insaf Ajili.

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Author Insaf Ajili declares that she has no conflict of interest. Author Zahra Ramezanpanah declares that she has no conflict of interest. Author Malik Mallem declares that he has no conflict of interest. Author Jean Yves Didier declares that he has no conflict of interest.

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Ajili, I., Ramezanpanah, Z., Mallem, M. et al. Expressive motions recognition and analysis with learning and statistical methods. Multimed Tools Appl 78, 16575–16600 (2019). https://doi.org/10.1007/s11042-018-6893-5

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  • DOI: https://doi.org/10.1007/s11042-018-6893-5

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