Classification techniques’ performance evaluation for facial expression recognition
| Autor: | Rizgar R. Zebari, Mayyadah R. Mahmood, Hivi Ismat Dino, Subhi R. M. Zeebaree, Maiwan B. Abdulrazaq, Abbas Kh. Ibrahim |
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| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
Computer Networks and Communications Computer science Decision tree 02 engineering and technology 010402 general chemistry 01 natural sciences k-nearest neighbors algorithm Base function 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering business.industry Multi-layer perceptron KNearest neighbor Pattern recognition 0104 chemical sciences Random forest Support vector machine Chi-square feature selection Facial expression recognition Hardware and Architecture Multilayer perceptron Signal Processing 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 21:1176 |
| ISSN: | 2502-4760 2502-4752 |
| DOI: | 10.11591/ijeecs.v21.i2.pp1176-1184 |
| Popis: | Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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