{"title":"基于gmm的SVM人脸识别","authors":"H. Bredin, N. Dehak, G. Chollet","doi":"10.1109/ICPR.2006.611","DOIUrl":null,"url":null,"abstract":"A new face recognition algorithm is presented. It supposes that a video sequence of a person is available both at enrollment and test time. During enrollment, a client Gaussian mixture model (GMM) is adapted from a world GMM using eigenface features extracted from each frame of the video. Then, a support vector machine (SVM) is used to find a decision border between the client GMM and pseudo-impostors GMMs. At test time, a GMM is adapted from the test video and a decision is taken using the previously learned client SVM. This algorithm brings a 3.5% equal error rate (EER) improvement over the biosecure reference system on the Pooled protocol of the BANCA database","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":"{\"title\":\"GMM-based SVM for face recognition\",\"authors\":\"H. Bredin, N. Dehak, G. Chollet\",\"doi\":\"10.1109/ICPR.2006.611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new face recognition algorithm is presented. It supposes that a video sequence of a person is available both at enrollment and test time. During enrollment, a client Gaussian mixture model (GMM) is adapted from a world GMM using eigenface features extracted from each frame of the video. Then, a support vector machine (SVM) is used to find a decision border between the client GMM and pseudo-impostors GMMs. At test time, a GMM is adapted from the test video and a decision is taken using the previously learned client SVM. This algorithm brings a 3.5% equal error rate (EER) improvement over the biosecure reference system on the Pooled protocol of the BANCA database\",\"PeriodicalId\":236033,\"journal\":{\"name\":\"18th International Conference on Pattern Recognition (ICPR'06)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"18th International Conference on Pattern Recognition (ICPR'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2006.611\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"18th International Conference on Pattern Recognition (ICPR'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2006.611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new face recognition algorithm is presented. It supposes that a video sequence of a person is available both at enrollment and test time. During enrollment, a client Gaussian mixture model (GMM) is adapted from a world GMM using eigenface features extracted from each frame of the video. Then, a support vector machine (SVM) is used to find a decision border between the client GMM and pseudo-impostors GMMs. At test time, a GMM is adapted from the test video and a decision is taken using the previously learned client SVM. This algorithm brings a 3.5% equal error rate (EER) improvement over the biosecure reference system on the Pooled protocol of the BANCA database
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