M. Villegas, Roberto Paredes Palacios, Alfons Juan-Císcar, E. Vidal
{"title":"Face verification on color images using local features","authors":"M. Villegas, Roberto Paredes Palacios, Alfons Juan-Císcar, E. Vidal","doi":"10.1109/CVPRW.2008.4563123","DOIUrl":null,"url":null,"abstract":"In this paper we propose a probabilistic model for the local features technique which provides a methodology to improve this approach. On the other hand, a method for compensating the color variability in images is adapted for the local feature model. Finally, an experimental study is made in order to evaluate the performance of the local features approach on challenging situations such as partially occluded images and having only one training image per user. The results of the experiments are competitive with state-of-the-art algorithms even when we have the mentioned extreme situations.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2008.4563123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
In this paper we propose a probabilistic model for the local features technique which provides a methodology to improve this approach. On the other hand, a method for compensating the color variability in images is adapted for the local feature model. Finally, an experimental study is made in order to evaluate the performance of the local features approach on challenging situations such as partially occluded images and having only one training image per user. The results of the experiments are competitive with state-of-the-art algorithms even when we have the mentioned extreme situations.