{"title":"基于分布估计的非接触式手部识别","authors":"J. Doublet, O. Lepetit, M. Revenu","doi":"10.1109/BCC.2007.4430547","DOIUrl":null,"url":null,"abstract":"More and more research have been developed very recently for automatic hand recognition. This paper proposes a new method for contactless hand authentication in complex images with low cost devices. Our system uses skin color and hand shape information for hand detection process. Next, the palm is extracted and characterized by a bank of Gabor filters. Finally, the palm features are compared with a distribution estimation given an optimal discrimination. The experimental results present an error rate lower than 1.7% with a population of 49 people.","PeriodicalId":389417,"journal":{"name":"2007 Biometrics Symposium","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Contactless Hand Recognition Based on Distribution Estimation\",\"authors\":\"J. Doublet, O. Lepetit, M. Revenu\",\"doi\":\"10.1109/BCC.2007.4430547\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"More and more research have been developed very recently for automatic hand recognition. This paper proposes a new method for contactless hand authentication in complex images with low cost devices. Our system uses skin color and hand shape information for hand detection process. Next, the palm is extracted and characterized by a bank of Gabor filters. Finally, the palm features are compared with a distribution estimation given an optimal discrimination. The experimental results present an error rate lower than 1.7% with a population of 49 people.\",\"PeriodicalId\":389417,\"journal\":{\"name\":\"2007 Biometrics Symposium\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 Biometrics Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BCC.2007.4430547\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 Biometrics Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BCC.2007.4430547","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Contactless Hand Recognition Based on Distribution Estimation
More and more research have been developed very recently for automatic hand recognition. This paper proposes a new method for contactless hand authentication in complex images with low cost devices. Our system uses skin color and hand shape information for hand detection process. Next, the palm is extracted and characterized by a bank of Gabor filters. Finally, the palm features are compared with a distribution estimation given an optimal discrimination. The experimental results present an error rate lower than 1.7% with a population of 49 people.
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