{"title":"Facial Expression Recognition using Bandlet Transform and Centre Symmetric – Local Binary Pattern","authors":"Gaurav V. Deshmukh, S. Bhandari","doi":"10.1109/SPIN.2018.8474037","DOIUrl":null,"url":null,"abstract":"Humans interact socially with the help of facial expressions. Even health states or pains are reflected through facial expressions and hence can be useful in healthcare. Here, a facial expression recognition system is proposed. The bandlet transform is performed on face image to generate quadtree. Then on the output of bandlet transform centre symmetric - local binary pattern (CS-LBP) is applied. A feature vector of the image is generated by taking the histogram of CS-LBP. The support vector machine (SVM) is used to classify expressions in six categories. The experiments are performed using a publically available CK+ dataset. The initial results with LBP and CS-LBP are reported.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPIN.2018.8474037","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
Humans interact socially with the help of facial expressions. Even health states or pains are reflected through facial expressions and hence can be useful in healthcare. Here, a facial expression recognition system is proposed. The bandlet transform is performed on face image to generate quadtree. Then on the output of bandlet transform centre symmetric - local binary pattern (CS-LBP) is applied. A feature vector of the image is generated by taking the histogram of CS-LBP. The support vector machine (SVM) is used to classify expressions in six categories. The experiments are performed using a publically available CK+ dataset. The initial results with LBP and CS-LBP are reported.