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A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition

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@article{IJASEIT81,
   author = {Amir Jamshidnezhad and Md Jan Nordin},
   title = {A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {1},
   number = {4},
   year = {2011},
   pages = {391--394},
   keywords = {Facial expression recognition; fuzzy rule based system; genetic algorithm; classifier},
   abstract = {In recent decades computer technology has considerable developed in use of intelligent systems for classification. The development of HCI systems is highly depended on accurate understanding of emotions. However, facial expressions are difficult to classify by a mathematical models because of natural quality. In this paper, quantitative analysis is used in order to find the most effective features movements between the selected facial feature points. Therefore, the features are extracted not only based on the psychological studies, but also based on the quantitative methods to arise the accuracy of recognitions. Also in this model, fuzzy logic and genetic algorithm are used to classify facial expressions. Genetic algorithm is an exclusive attribute of proposed model which is used for tuning membership functions and increasing the accuracy.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=81},
   doi = {10.18517/ijaseit.1.4.81}
}

EndNote

%A Jamshidnezhad, Amir
%A Nordin, Md Jan
%D 2011
%T A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition
%B 2011
%9 Facial expression recognition; fuzzy rule based system; genetic algorithm; classifier
%! A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition
%K Facial expression recognition; fuzzy rule based system; genetic algorithm; classifier
%X In recent decades computer technology has considerable developed in use of intelligent systems for classification. The development of HCI systems is highly depended on accurate understanding of emotions. However, facial expressions are difficult to classify by a mathematical models because of natural quality. In this paper, quantitative analysis is used in order to find the most effective features movements between the selected facial feature points. Therefore, the features are extracted not only based on the psychological studies, but also based on the quantitative methods to arise the accuracy of recognitions. Also in this model, fuzzy logic and genetic algorithm are used to classify facial expressions. Genetic algorithm is an exclusive attribute of proposed model which is used for tuning membership functions and increasing the accuracy.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=81
%R doi:10.18517/ijaseit.1.4.81
%J International Journal on Advanced Science, Engineering and Information Technology
%V 1
%N 4
%@ 2088-5334

IEEE

Amir Jamshidnezhad and Md Jan Nordin,"A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition," International Journal on Advanced Science, Engineering and Information Technology, vol. 1, no. 4, pp. 391-394, 2011. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.1.4.81.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Jamshidnezhad, Amir
AU  - Nordin, Md Jan
PY  - 2011
TI  - A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 1 (2011) No. 4
Y2  - 2011
SP  - 391
EP  - 394
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Facial expression recognition; fuzzy rule based system; genetic algorithm; classifier
N2  - In recent decades computer technology has considerable developed in use of intelligent systems for classification. The development of HCI systems is highly depended on accurate understanding of emotions. However, facial expressions are difficult to classify by a mathematical models because of natural quality. In this paper, quantitative analysis is used in order to find the most effective features movements between the selected facial feature points. Therefore, the features are extracted not only based on the psychological studies, but also based on the quantitative methods to arise the accuracy of recognitions. Also in this model, fuzzy logic and genetic algorithm are used to classify facial expressions. Genetic algorithm is an exclusive attribute of proposed model which is used for tuning membership functions and increasing the accuracy.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=81
DO  - 10.18517/ijaseit.1.4.81

RefWorks

RT Journal Article
ID 81
A1 Jamshidnezhad, Amir
A1 Nordin, Md Jan
T1 A Classifier Model based on the Features Quantitative Analysis for Facial Expression Recognition
JF International Journal on Advanced Science, Engineering and Information Technology
VO 1
IS 4
YR 2011
SP 391
OP 394
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Facial expression recognition; fuzzy rule based system; genetic algorithm; classifier
AB In recent decades computer technology has considerable developed in use of intelligent systems for classification. The development of HCI systems is highly depended on accurate understanding of emotions. However, facial expressions are difficult to classify by a mathematical models because of natural quality. In this paper, quantitative analysis is used in order to find the most effective features movements between the selected facial feature points. Therefore, the features are extracted not only based on the psychological studies, but also based on the quantitative methods to arise the accuracy of recognitions. Also in this model, fuzzy logic and genetic algorithm are used to classify facial expressions. Genetic algorithm is an exclusive attribute of proposed model which is used for tuning membership functions and increasing the accuracy.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=81
DO  - 10.18517/ijaseit.1.4.81