Recognizing human emotional states from body movement

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.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher.institution

Science

Year dc:date.issued

2019

Author dc:creator

• Ahmed, Ferdous

Advisor dc:contributor.advisor

• Gavrilova, Marina L.

Committee members dc:contributor.committeemember

• Korobenko, Artem
• Rokne, Jon G.

• Emotion Recognition
• Body Movement

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/36735

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/110620