{"title":"Multimodal expression-invariant face recognition using dual-tree complex wavelet transform","authors":"Fazael Ayatollahi, A. Raie, F. Hajati","doi":"10.1109/IRANIANMVIP.2013.6779969","DOIUrl":null,"url":null,"abstract":"A new multimodal face recognition method which extracts features of rigid and semi-rigid regions of the face using Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed. DT-CWT decomposes range and intensity images into eight sub-images consisting of six band-pass sub-images to represent face details and two low-pass sub-images to represent face approximates. In this work, support vector machine (SVM) has been used as the classifier. The proposed method has been evaluated using the face BU-3DFE dataset containing a wide range of expression changes. Findings include the overall identification rate of 98.1% and the overall verification rate of 99.3% at 0.1% false acceptance rate.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRANIANMVIP.2013.6779969","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
A new multimodal face recognition method which extracts features of rigid and semi-rigid regions of the face using Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed. DT-CWT decomposes range and intensity images into eight sub-images consisting of six band-pass sub-images to represent face details and two low-pass sub-images to represent face approximates. In this work, support vector machine (SVM) has been used as the classifier. The proposed method has been evaluated using the face BU-3DFE dataset containing a wide range of expression changes. Findings include the overall identification rate of 98.1% and the overall verification rate of 99.3% at 0.1% false acceptance rate.