{"title":"一种三维人脸定位的混合方法","authors":"Qaim Mehdi Rizvi, Qamar Abbas, Hasan Ahmad","doi":"10.1145/2007052.2007065","DOIUrl":null,"url":null,"abstract":"Face localization is always a challenging job for a good recognition scheme. If process has to be done for 3D faces, it is quit difficult to localize faces. In this paper we provide a face localization scheme which is based on Average Threshold Value (AVT) and Grayscale Pixel Value Band (GPVB) with the help of Histogram Equalization. We experiment this approach on a synthetic 3D morphable face model of 20 individuals and get 89.51% accuracy among 20 faces with same lighting condition, which is appreciative for further work on same stream.","PeriodicalId":348804,"journal":{"name":"International Conference on Advances in Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A hybrid method for 3D face localization\",\"authors\":\"Qaim Mehdi Rizvi, Qamar Abbas, Hasan Ahmad\",\"doi\":\"10.1145/2007052.2007065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Face localization is always a challenging job for a good recognition scheme. If process has to be done for 3D faces, it is quit difficult to localize faces. In this paper we provide a face localization scheme which is based on Average Threshold Value (AVT) and Grayscale Pixel Value Band (GPVB) with the help of Histogram Equalization. We experiment this approach on a synthetic 3D morphable face model of 20 individuals and get 89.51% accuracy among 20 faces with same lighting condition, which is appreciative for further work on same stream.\",\"PeriodicalId\":348804,\"journal\":{\"name\":\"International Conference on Advances in Computing and Artificial Intelligence\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Advances in Computing and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2007052.2007065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Advances in Computing and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2007052.2007065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Face localization is always a challenging job for a good recognition scheme. If process has to be done for 3D faces, it is quit difficult to localize faces. In this paper we provide a face localization scheme which is based on Average Threshold Value (AVT) and Grayscale Pixel Value Band (GPVB) with the help of Histogram Equalization. We experiment this approach on a synthetic 3D morphable face model of 20 individuals and get 89.51% accuracy among 20 faces with same lighting condition, which is appreciative for further work on same stream.
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