13 April 2018 Laban movement analysis to classify emotions from motion
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106962Q (2018) https://doi.org/10.1117/12.2309451
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Abstract
In this paper, we present the study of Laban Movement Analysis (LMA) to understand basic human emotions from nonverbal human behaviors. While there are a lot of studies on understanding behavioral patterns based on natural language processing and speech processing applications, understanding emotions or behavior from non-verbal human motion is still a very challenging and unexplored field. LMA provides a rich overview of the scope of movement possibilities. These basic elements can be used for generating movement or for describing movement. They provide an inroad to understanding movement and for developing movement efficiency and expressiveness. Each human being combines these movement factors in his/her own unique way and organizes them to create phrases and relationships which reveal personal, artistic, or cultural style. In this work, we build a motion descriptor based on a deep understanding of Laban theory. The proposed descriptor builds up on previous works and encodes experiential features by using temporal windows. We present a more conceptually elaborate formulation of Laban theory and test it in a relatively new domain of behavioral research with applications in human-machine interaction. The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. We test our algorithm on UCLIC dataset which consists of body motions of 13 non-professional actors portraying angry, fear, happy and sad emotions. We achieve an accuracy of 87.30% on this dataset.
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Swati Dewan, Swati Dewan, Shubham Agarwal, Shubham Agarwal, Navjyoti Singh, Navjyoti Singh, } "Laban movement analysis to classify emotions from motion", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962Q (13 April 2018); doi: 10.1117/12.2309451; https://doi.org/10.1117/12.2309451
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