Recognizing human emotional states from body movement
Date
2019-07-09
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Abstract
An emotion-aware computer system capable of responding to expressive human gestures and movements can significantly change the dynamics of human-computer interaction. This thesis addresses the problem of the creation of a computer model capable of automatically discerning emotion using various motion-related features of the human body. The proposed emotion recognition model automatically identifies relevant motion features using a combination of filter-based feature selection methods and the power of genetic algorithms. In addition to recognizing emotions, this thesis also focuses on gaining a deeper understanding of the role that various motion features play in emotion recognition, the ability to express emotionally relevant information by various parts of the human body and the effects of various action scenarios on emotion recognition. Rigorous analysis conducted on a proprietary dataset shows that the proposed computer model is very effective at identifying human emotion based predominantly on motion-related information. The proposed emotion recognition system also outperforms existing state-of-the-art computer models for emotion recognition.
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Keywords
Emotion Recognition, Body Movement
Citation
Ahmed, F. (2019). Recognizing human emotional states from body movement (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.