{"title":"Gabor feature-based complete fisher discriminant framework for facial feature extraction","authors":"Zhiqiang Zeng","doi":"10.1109/WCSP.2009.5371732","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel feature extraction approach using Gabor feature based complete fisher discriminant algorithm (GCFD). Four main steps are involved in the proposed GCFD: (i) Gabor features of different scales and orientations are extracted by the convolution of Gabor filter bank and original gray images; (ii) Complete fisher discriminant algorithm (CFD) is used for feature dimensionality reduction and to extract all discrimination information; (iii) Feature fusion algorithm and Euclidean distance based nearest neighbor classifier are finally used for classification. (iv)Simulation results show the effectiveness of our proposed GCFD.","PeriodicalId":244652,"journal":{"name":"2009 International Conference on Wireless Communications & Signal Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Wireless Communications & Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCSP.2009.5371732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose a novel feature extraction approach using Gabor feature based complete fisher discriminant algorithm (GCFD). Four main steps are involved in the proposed GCFD: (i) Gabor features of different scales and orientations are extracted by the convolution of Gabor filter bank and original gray images; (ii) Complete fisher discriminant algorithm (CFD) is used for feature dimensionality reduction and to extract all discrimination information; (iii) Feature fusion algorithm and Euclidean distance based nearest neighbor classifier are finally used for classification. (iv)Simulation results show the effectiveness of our proposed GCFD.