Emotion recognition on selected facial landmarks using supervised learning algorithms

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creativework.keywordsemotion Recognition; facial landmarks; feature selection; supervised learning algorithms
dc.contributor.authorBaculo, Maria Jeseca
dc.contributor.authorAzcarraga, Judith
dc.date.accessioned2026-09-01T05:41:26Z
dc.date.available2026-09-01T05:41:26Z
dc.date.issued2018-07
dc.descriptionAbstract only
dc.description.abstractFacial landmarks may be used to localize the movement of facial muscles that help identify an emotion. It is important that these points are appropriately represented to achieve a successful emotion Recognition rate. In this paper, the extraction of 68 facial landmarks, normalization methods and classification of 7 basic emotions are presented. The Cohn-Kanade Database is used as a test bed for the different emotion Recognition tasks. The images are normalized by transforming the inputs based on similarity (CKCT) and the mean shape (CKMS). Forward Search and Principal Component Analysis are used to identify the most important features among the 68 facial points. Decision Tree, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron algorithms are used in building classifiers on reduced and complete feature set. It is interesting to note that facial points in the mouth area are found to be significant in the classification of emotions. © 2018 IEEE.
dc.identifier.citationBaculo, M. J. & Azcarraga, J. (2018). Emotion Recognition on Selected Facial Landmarks Using Supervised Learning Algorithms. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, 1483-1489. https://doi.org/10.1109/SMC.2018.00258
dc.identifier.doihttps://doi.org/10.1109/SMC.2018.00258
dc.identifier.isbn978-153866650-0
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2434
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.sdgSDG 9
dc.sdgSDG 3
dc.titleEmotion recognition on selected facial landmarks using supervised learning algorithms
dc.typeConference paper
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