Indonesian Journal of Electrical Engineering and Computer Science
Open accessAbstract
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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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916 · see citing works
A performance evaluation of local descriptors
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Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367 · see citing works
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Comparative Analysis of Image Quality Assessment Metrics: MSE, PSNR, SSIM and FSIM
Al Najjar, Y. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24302013533 · Cited by 37 · see citing works
- Authors
- Mahmood, M. R., Abdulrazzaq, M. B., Zeebaree, S. R. M., Ibrahim, A. K., Zebari, R. R., & Dino, H. I.
- ORCID
- Venue
- Indonesian Journal of Electrical Engineering and Computer Science
- Published
- 2025-03-22
- ISSN
- 2502-4752 (ISSN-L)
- 2502-4760
- OpenAlex
- W4295314730
- Cited by
- 317
- References
- 3
- Citations by year
- 2025: 13 · 2024: 31 · 2023: 52 · 2022: 40 · 2021: 46 · 2020: 24 · 2019: 43 · 2018: 46 · 2017: 21 · 2016: 1
- Type
- paratext
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-sa
- Locations
- Indonesian Journal of Electrical Engineering and Computer Science (publishedVersion, cc-by-sa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- Universitas Brawijaya
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- gene expression data, high-dimensional classification, LASSO feature selection, classification and regression trees, bagging, ovarian tumor classification, majority voting, model stability, interpretable classifiers
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