{"title":"基于svm的人脸识别判别分析","authors":"Sangki Kim, K. Toh, Sangyoun Lee","doi":"10.1109/ICIEA.2008.4582892","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce a novel variant of Linear Discriminant Analysis (LDA) for face recognition. The proposed method attempts to find an optimal LDA matrix by redesigning the between-class scatter matrix incorporating a Support Vector Machine (SVM). Our empirical evaluations show that the proposed method offers noticeable performance improvement over the conventional LDA.","PeriodicalId":309894,"journal":{"name":"2008 3rd IEEE Conference on Industrial Electronics and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SVM-based Discriminant Analysis for face recognition\",\"authors\":\"Sangki Kim, K. Toh, Sangyoun Lee\",\"doi\":\"10.1109/ICIEA.2008.4582892\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we introduce a novel variant of Linear Discriminant Analysis (LDA) for face recognition. The proposed method attempts to find an optimal LDA matrix by redesigning the between-class scatter matrix incorporating a Support Vector Machine (SVM). Our empirical evaluations show that the proposed method offers noticeable performance improvement over the conventional LDA.\",\"PeriodicalId\":309894,\"journal\":{\"name\":\"2008 3rd IEEE Conference on Industrial Electronics and Applications\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 3rd IEEE Conference on Industrial Electronics and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2008.4582892\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 3rd IEEE Conference on Industrial Electronics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2008.4582892","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SVM-based Discriminant Analysis for face recognition
In this paper, we introduce a novel variant of Linear Discriminant Analysis (LDA) for face recognition. The proposed method attempts to find an optimal LDA matrix by redesigning the between-class scatter matrix incorporating a Support Vector Machine (SVM). Our empirical evaluations show that the proposed method offers noticeable performance improvement over the conventional LDA.
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