{"title":"双像素特征在人脸检测中的应用","authors":"Ido Nissenboim, D. Keren, M. Werman","doi":"10.1109/SITIS.2008.28","DOIUrl":null,"url":null,"abstract":"We propose a \"quick rejection\" paradigm which is applied to face detection. The features we use are arguably the simplest possible: a threshold on the difference between the grey levels of two pixels. No negative examples are used for training; instead, we use a simple statistical model of natural images. The resulting features are easy to find, extremely fast to apply, and achieve a good detection rate.","PeriodicalId":202698,"journal":{"name":"2008 IEEE International Conference on Signal Image Technology and Internet Based Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Applying Two-Pixel Features to Face Detection\",\"authors\":\"Ido Nissenboim, D. Keren, M. Werman\",\"doi\":\"10.1109/SITIS.2008.28\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a \\\"quick rejection\\\" paradigm which is applied to face detection. The features we use are arguably the simplest possible: a threshold on the difference between the grey levels of two pixels. No negative examples are used for training; instead, we use a simple statistical model of natural images. The resulting features are easy to find, extremely fast to apply, and achieve a good detection rate.\",\"PeriodicalId\":202698,\"journal\":{\"name\":\"2008 IEEE International Conference on Signal Image Technology and Internet Based Systems\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Signal Image Technology and Internet Based Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SITIS.2008.28\",\"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 IEEE International Conference on Signal Image Technology and Internet Based Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITIS.2008.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We propose a "quick rejection" paradigm which is applied to face detection. The features we use are arguably the simplest possible: a threshold on the difference between the grey levels of two pixels. No negative examples are used for training; instead, we use a simple statistical model of natural images. The resulting features are easy to find, extremely fast to apply, and achieve a good detection rate.