TY  - BOOK
AU  - Akhgar,Mahsa
AU  - Zarbakhsh,Payam
TI  - 2D-3D FACIAL EXPRESSION RECOGNITION BU GRAPH FORMING
PY  - 2023///
KW  -  Facial expression
KW  - Dissertations, Academic
KW  -  Three-dimensional imaging
N1  - Thesis  (MSc) - Cyprus International Univesity. Institute of Graduate Studies and Research Electrical and Electronics Engineering; Includes bibliography (sheets 77-84)
N2  - ABSTRACT
The need for biometric data analysis is an integral part of today’s digital world. With 
advances in data registration and machine learning techniques, developing image 
processing technologies such as identity recognition and facial expression detection 
has attracted a significant intersect among the research and tech community. Emotion 
detection from facial images has many applications ranging from surveillance and 
security to psychology and marketing. These systems, however, are prone to errors due 
to variable factors such as brightness intensity or changes in facial tissue. In this study, 
a novel graph forming method based on invariable points of human face is proposed. 
These points are called invariable because they are robust to the changes in brightness 
and texture. Emotions are manifested in the proposed graph-based features extracted 
from 2D and 3D images. On top of the feature extraction component, we also assessed 
the effect of applying a feature selection method based on minimum redundancy and 
maximum relevance. Three well-known classifiers namely SVM, Random Forest (RF) 
and kNN are used to detect the labels. The results confirm the effectiveness of the 
feature selection stage. Moreover, the findings imply that graph-based features from 
3D images result in a better performance. When comparing the classifiers, SVM is the 
winner followed by the RF. The best results are achieved when graph-based features 
from 2D and 3D images are concatenated. In overall, the recognition rate of the 
proposed method is superior to the state of the art.
Keywords: 2D+3D Images; Facial Expression Recognition Geometric; Facial 
Features; kNN Classifier; RF Method; SVM classifier
ER  -