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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| Online Access: | Get fulltext |
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| LEADER | 02490 am a22003493u 4500 | ||
|---|---|---|---|
| 001 | ijeecs23150_14657 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Mahmood, Mayyadah R. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Abdulrazaq, Maiwan B. |e author |
| 700 | 1 | 0 | |a Zeebaree, Subhi R. M. |e author |
| 700 | 1 | 0 | |a Ibrahim, Abbas Kh. |e author |
| 700 | 1 | 0 | |a Zebari, Rizgar Ramadhan |e author |
| 700 | 1 | 0 | |a Dino, Hivi Ismat |e author |
| 245 | 0 | 0 | |a Classification techniques' performance evaluation for facial expression recognition |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-02-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23150 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Base function; Chi-square feature selection; Facial expression recognition; K-nearest neighbor; Multi-layer perceptron | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 21, No 2: February 2021; 1176-1184 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v21.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23150/14657 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23150/14657 |z Get fulltext |