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In the pre-processing stage a method based on edge detectors and thresholding operators for eyebrow and mouth segmentation is proposed; the next stage is feature extraction, we propose using polynomials as features for describing eyebrows and mouth regions. Finally, in classification stage different supervised learners such as: Neural Networks, K-Nearest Neighbors and C4.5 decision trees are tested in order to obtain a model for classifying three out of six basic emotions (anger, happiness and surprise). According to our results, the proposed approach has acceptable accuracy for predicting new examples.<\/jats:p>","DOI":"10.3233\/jifs-169496","type":"journal-article","created":{"date-parts":[[2018,5,22]],"date-time":"2018-05-22T10:54:06Z","timestamp":1526986446000},"page":"3119-3131","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Mouth and eyebrow segmentation for emotion recognition using interpolated polynomials"],"prefix":"10.1177","volume":"34","author":[{"given":"Jes\u00fas","family":"Garc\u00eda-Ram\u00edrez","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Benem\u00e9rita Universidad Aut\u00f3noma de Puebla, Puebla, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. 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