{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T16:52:38Z","timestamp":1742403158363},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,4,16]],"date-time":"2019-04-16T00:00:00Z","timestamp":1555372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"General Direction of Scientific Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Emotion recognition is a key work of research area in brain computer interactions. With the increasing concerns about affective computing, emotion recognition has attracted more and more attention in the past decades. Focusing on geometric positions of key parts of the face and well detecting them is the best way to increase accuracy of emotion recognition systems and reach high classification rates. In this paper, we propose a hybrid system based on wavelet networks using 1D Fast Wavelet Transform. This system combines two approaches: the biometric distances approach where we propose a new technique to locate feature points and the wrinkles approach where we propose a new method to locate the wrinkles regions in the face. The classification rates given by experimental results show the effectiveness of our proposed approach compared to other methods.<\/jats:p>","DOI":"10.1093\/comjnl\/bxz032","type":"journal-article","created":{"date-parts":[[2019,3,19]],"date-time":"2019-03-19T09:10:50Z","timestamp":1552986650000},"page":"351-363","source":"Crossref","is-referenced-by-count":2,"title":["Emotion Recognition by a Hybrid System Based on the Features of Distances and the Shapes of the Wrinkles"],"prefix":"10.1093","volume":"63","author":[{"given":"Rim","family":"Afdhal","sequence":"first","affiliation":[{"name":"Research Team on Intelligent Machines, National School of Engineers of Gabes, University of Gabes, Gabes, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ridha","family":"Ejbali","sequence":"first","affiliation":[{"name":"Research Team on Intelligent Machines, National School of Engineers of Gabes, University of Gabes, Gabes, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mourad","family":"Zaied","sequence":"first","affiliation":[{"name":"Research Team on Intelligent Machines, National School of Engineers of Gabes, University of Gabes, Gabes, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,4,16]]},"reference":[{"key":"2020042105454553200_bxz032C1","author":"Sokolov","year":"2018"},{"key":"2020042105454553200_bxz032C2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.patrec.2018.04.010","article-title":"Hybrid deep neural networks for face emotion recognition","volume":"115","author":"Jain","year":"2018","journal-title":"Pattern Recognit. 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