Classification techniques' performance evaluation for facial expression recognition
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 e...
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| Main Authors: | , , , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2021-02-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| Summary: | 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. |
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| Item Description: | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23150 |