{"title":"基于二维主成分分析和支持向量机的人脸性别识别","authors":"L. Bui, D. Tran, Xu Huang, G. Chetty","doi":"10.1109/NSS.2010.19","DOIUrl":null,"url":null,"abstract":"This paper presents a novel method for solving face gender recognition problem. This method employs 2D Principal Component Analysis, one of the prominent methods for extracting feature vectors, and Support Vector Machine, the most powerful discriminative method for classification. Experiments for the proposed approach have been conducted on FERET data set and the results show that the proposed method could improve the classification rates.","PeriodicalId":127173,"journal":{"name":"2010 Fourth International Conference on Network and System Security","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Face Gender Recognition Based on 2D Principal Component Analysis and Support Vector Machine\",\"authors\":\"L. Bui, D. Tran, Xu Huang, G. Chetty\",\"doi\":\"10.1109/NSS.2010.19\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel method for solving face gender recognition problem. This method employs 2D Principal Component Analysis, one of the prominent methods for extracting feature vectors, and Support Vector Machine, the most powerful discriminative method for classification. Experiments for the proposed approach have been conducted on FERET data set and the results show that the proposed method could improve the classification rates.\",\"PeriodicalId\":127173,\"journal\":{\"name\":\"2010 Fourth International Conference on Network and System Security\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Fourth International Conference on Network and System Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NSS.2010.19\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Fourth International Conference on Network and System Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSS.2010.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Face Gender Recognition Based on 2D Principal Component Analysis and Support Vector Machine
This paper presents a novel method for solving face gender recognition problem. This method employs 2D Principal Component Analysis, one of the prominent methods for extracting feature vectors, and Support Vector Machine, the most powerful discriminative method for classification. Experiments for the proposed approach have been conducted on FERET data set and the results show that the proposed method could improve the classification rates.
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