{"title":"基于多种面部特征的人脸识别","authors":"Rui Liao, S. Li","doi":"10.1109/AFGR.2000.840641","DOIUrl":null,"url":null,"abstract":"An automatic face recognition system based on multiple facial features is described. Each facial feature is represented by a Gabor-based complex vector and is localized by an automatic facial feature detection scheme. Two face recognition approaches, named two-layer nearest neighbor (TLNN) and modular nearest feature line (MNFL) respectively, are proposed. Both TLNN and MNFL are based on the multiple facial features detected for each image and their superiority in face recognition is demonstrated.","PeriodicalId":360065,"journal":{"name":"Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":"{\"title\":\"Face recognition based on multiple facial features\",\"authors\":\"Rui Liao, S. Li\",\"doi\":\"10.1109/AFGR.2000.840641\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An automatic face recognition system based on multiple facial features is described. Each facial feature is represented by a Gabor-based complex vector and is localized by an automatic facial feature detection scheme. Two face recognition approaches, named two-layer nearest neighbor (TLNN) and modular nearest feature line (MNFL) respectively, are proposed. Both TLNN and MNFL are based on the multiple facial features detected for each image and their superiority in face recognition is demonstrated.\",\"PeriodicalId\":360065,\"journal\":{\"name\":\"Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)\",\"volume\":\"91 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AFGR.2000.840641\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AFGR.2000.840641","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Face recognition based on multiple facial features
An automatic face recognition system based on multiple facial features is described. Each facial feature is represented by a Gabor-based complex vector and is localized by an automatic facial feature detection scheme. Two face recognition approaches, named two-layer nearest neighbor (TLNN) and modular nearest feature line (MNFL) respectively, are proposed. Both TLNN and MNFL are based on the multiple facial features detected for each image and their superiority in face recognition is demonstrated.
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