{"title":"A probabilistic union approach to robust face recognition with partial distortion and occlusion","authors":"Jie Lin, J. Ming, D. Crookes","doi":"10.1109/ICASSP.2008.4517779","DOIUrl":null,"url":null,"abstract":"This paper presents a new approach to face recognition where the images are subject to unknown, partial distortion/occlusion. The new approach is a probabilistic decision-based neural network (PDBNN), built on a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the matched local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN has been evaluated on two face image databases, XM2VTS and ORL, using testing images subjected to various types of partial distortion and occlusion. The new system has demonstrated improved performance over other systems.","PeriodicalId":333742,"journal":{"name":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2008.4517779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
This paper presents a new approach to face recognition where the images are subject to unknown, partial distortion/occlusion. The new approach is a probabilistic decision-based neural network (PDBNN), built on a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the matched local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN has been evaluated on two face image databases, XM2VTS and ORL, using testing images subjected to various types of partial distortion and occlusion. The new system has demonstrated improved performance over other systems.