Laban descriptors for gesture recognition and emotional analysis

Abstract : In this paper, we introduce a new set of 3D gesture descriptors based on the labanmovement analysis model. The proposed descriptors are used in a machine learning framework (with SVM and different random forest techniques) for both gesture recognition and emotional analysis purposes. In a first experiment, we test our expressivity model for action recognition purposes on the Microsoft Research Cambridge-12 dataset and obtain very high recognition rates (more than 97 %). In a second experiment, we test our descriptors' ability to qualify the emotional content, upon a database of pre-segmented orchestra conductors' gestures recorded in rehearsals. The results obtained show the relevance of our model which outperforms results reported in similar works on emotion recognition
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Article dans une revue
Visual Computer, Springer Verlag, 2016, 32 (1), pp.83 - 98. 〈10.1007/s00371-014-1057-8〉
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https://hal.archives-ouvertes.fr/hal-01575294
Contributeur : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Soumis le : vendredi 18 août 2017 - 16:59:07
Dernière modification le : jeudi 31 mai 2018 - 09:12:02

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Arthur Truong, Hugo Boujut, Titus Zaharia. Laban descriptors for gesture recognition and emotional analysis. Visual Computer, Springer Verlag, 2016, 32 (1), pp.83 - 98. 〈10.1007/s00371-014-1057-8〉. 〈hal-01575294〉

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