An ANN based combined classifier approach for facial emotion recognition

Yağış, Ekin (2018) An ANN based combined classifier approach for facial emotion recognition. [Thesis]

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Facial expressions are the simplest reflections of human emotions, which are at the same time an integral part of any communication. Over the last decade, facial emotion recognition has attracted a great deal of research interest due to its various applications in the fields such as human computer interaction, robotics and data analytics. In this thesis, we present a facial emotion recognition approach that is based on facial expressions to classify seven emotional states: neutral, joy, sadness, surprise, anger, fear and disgust. To perform classification, two different facial features called Action Units (AUs) and Feature Point Positions (FPPs) are extracted from image sequences. A depth camera is used to capture image sequences collected from 13 volunteers to classify seven emotional states. Having extracted two sets of features, separate artificial neural network classifiers are trained. Logarithmic Opinion Pool (LOP) is then employed to combine the decision probabilities coming from each classifier. Experimental results are quite promising and establish a basis for future work on the topic.
Item Type: Thesis
Uncontrolled Keywords: Emotion recognition. -- Facial expression analysis. -- Facial action coding system. -- Classification. -- Artificial Neural Networks (ANN). -- Logarithmic Opinion Pool (LOP). -- Duygu tanıma. -- Yüz ifade analizi. -- Yüz hareketleri kodlama sistemi. -- Sınıflandırma. -- Yapay sinir ağları. -- Logaritmik düşünce havuzu.
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 02 Oct 2018 14:10
Last Modified: 26 Apr 2022 10:25

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