{"title":"Concentration analysis by detecting face features of learners","authors":"Seunghui Cha, Wookhyun Kim","doi":"10.1109/PACRIM.2015.7334807","DOIUrl":null,"url":null,"abstract":"The paper presents an analysis on the concentration of learning. By capturing video images of students, the proposed method detects and analyzes facial features from the image data and determines the state of learner's concentration. Since the concentration is important to the learners, this method is applied to the classrooms. First, feature points are generated from the face and then feature points of the face are used to determine non-focused state. The length of the front face is used to make a decision for the face change. The coordinate value of the facial center is used to decide the face turns. The criteria value of the opened eye is used to decide whether the closed eyes or the opened eyes. Through the experiments, the proposed method detects the concentration up to 90%.","PeriodicalId":350052,"journal":{"name":"2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM.2015.7334807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The paper presents an analysis on the concentration of learning. By capturing video images of students, the proposed method detects and analyzes facial features from the image data and determines the state of learner's concentration. Since the concentration is important to the learners, this method is applied to the classrooms. First, feature points are generated from the face and then feature points of the face are used to determine non-focused state. The length of the front face is used to make a decision for the face change. The coordinate value of the facial center is used to decide the face turns. The criteria value of the opened eye is used to decide whether the closed eyes or the opened eyes. Through the experiments, the proposed method detects the concentration up to 90%.