000			02835nam a22002897a 4500
003			KOHA
005			20230418085925.0
008			230323d2023 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 2835 _bA44 2023
100	1		_aAkhgar, Mahsa
245	1	0	_a2D-3D FACIAL EXPRESSION RECOGNITION BU GRAPH FORMING / _cMAHSA AKHGAR; SUPERVISOR: Asst. Prof. Dr. PAYAM ZARBAKHSH
264			_c2023
300			_axi, 84 sheets: _bphotos; _c31 cm. _eIncludes CD
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International Univesity. Institute of Graduate Studies and Research Electrical and Electronics Engineering
504			_aIncludes bibliography (sheets 77-84)
520			_aABSTRACT 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.
650		0	_a Facial expression _vDissertations, Academic
650		0	_a Three-dimensional imaging _vDissertations, Academic
700	1		_aZarbakhsh, Payam _esupervisor
942			_2ddc _cTS
999			_c290043 _d290043