{"title":"基于带状波变换和中心对称局部二值模式的面部表情识别","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":"{\"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}","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}
Facial Expression Recognition using Bandlet Transform and Centre Symmetric – Local Binary Pattern
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.