{"title":"基于Gabor特征的贝叶斯人脸识别","authors":"Xiaogang Wang, Xiaoou Tang","doi":"10.1145/982507.982521","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a new face recognition approach combining a Bayesian probabilistic model and Gabor filter responses. Since both the Bayesian algorithm and the Gabor features can reduce intrapersonal variation through different mechanisms, we integrate the two methods to take full advantage of both approaches. The efficacy of the new method is demonstrated by the experiments on 1180 face images from the XM2VTS database and 1260 face images from the AR database.","PeriodicalId":228135,"journal":{"name":"Workshop Brasileira em Métodos Agile / Brazilian Workshop on Agile Methods","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"44","resultStr":"{\"title\":\"Bayesian face recognition using Gabor features\",\"authors\":\"Xiaogang Wang, Xiaoou Tang\",\"doi\":\"10.1145/982507.982521\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a new face recognition approach combining a Bayesian probabilistic model and Gabor filter responses. Since both the Bayesian algorithm and the Gabor features can reduce intrapersonal variation through different mechanisms, we integrate the two methods to take full advantage of both approaches. The efficacy of the new method is demonstrated by the experiments on 1180 face images from the XM2VTS database and 1260 face images from the AR database.\",\"PeriodicalId\":228135,\"journal\":{\"name\":\"Workshop Brasileira em Métodos Agile / Brazilian Workshop on Agile Methods\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"44\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop Brasileira em Métodos Agile / Brazilian Workshop on Agile Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/982507.982521\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop Brasileira em Métodos Agile / Brazilian Workshop on Agile Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/982507.982521","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we propose a new face recognition approach combining a Bayesian probabilistic model and Gabor filter responses. Since both the Bayesian algorithm and the Gabor features can reduce intrapersonal variation through different mechanisms, we integrate the two methods to take full advantage of both approaches. The efficacy of the new method is demonstrated by the experiments on 1180 face images from the XM2VTS database and 1260 face images from the AR database.
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