Hafidh Fikri Rasyid, Kurniawan Nur Ramadhani, F. Sthevanie
{"title":"利用局部二值模式特征提取人脸图像进行蒙古人种和非蒙古人种分类","authors":"Hafidh Fikri Rasyid, Kurniawan Nur Ramadhani, F. Sthevanie","doi":"10.1109/ICOICT.2018.8528783","DOIUrl":null,"url":null,"abstract":"One of the areas on the human body that has the most dominant racial trait is the face. This research build the classification system for Mongoloid and non-Mongoloid race based on the area in the periorbital area of facial image. We use Local Binary Pattern to extract texture features on periorbital facial area. To classify the LBP features, we use Support Vector Machine classifier. The accuracy obtained from the system is 99.38%.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Mongoloid and Non-Mongoloid Race Classification from Face Image Using Local Binary Pattern Feature Extractions\",\"authors\":\"Hafidh Fikri Rasyid, Kurniawan Nur Ramadhani, F. Sthevanie\",\"doi\":\"10.1109/ICOICT.2018.8528783\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the areas on the human body that has the most dominant racial trait is the face. This research build the classification system for Mongoloid and non-Mongoloid race based on the area in the periorbital area of facial image. We use Local Binary Pattern to extract texture features on periorbital facial area. To classify the LBP features, we use Support Vector Machine classifier. The accuracy obtained from the system is 99.38%.\",\"PeriodicalId\":266335,\"journal\":{\"name\":\"2018 6th International Conference on Information and Communication Technology (ICoICT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 6th International Conference on Information and Communication Technology (ICoICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOICT.2018.8528783\",\"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 6th International Conference on Information and Communication Technology (ICoICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOICT.2018.8528783","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mongoloid and Non-Mongoloid Race Classification from Face Image Using Local Binary Pattern Feature Extractions
One of the areas on the human body that has the most dominant racial trait is the face. This research build the classification system for Mongoloid and non-Mongoloid race based on the area in the periorbital area of facial image. We use Local Binary Pattern to extract texture features on periorbital facial area. To classify the LBP features, we use Support Vector Machine classifier. The accuracy obtained from the system is 99.38%.