{"title":"A Software Framework for PCa-Based Face Recognition","authors":"Peng Peng, P. Alencar, D. Cowan","doi":"10.1109/SWSTE.2016.11","DOIUrl":null,"url":null,"abstract":"This paper focuses on a software framework to support face recognition, a specific area of image processing. For the processing approach, we use principal component analysis (PCA), a data dimensionality reduction approach. The goal of this study is to understand the entire face recognition process with PCA and to present a software framework supporting multiple variations, which can be used to help users create customized face recognition applications efficiently.","PeriodicalId":118525,"journal":{"name":"2016 IEEE International Conference on Software Science, Technology and Engineering (SWSTE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Software Science, Technology and Engineering (SWSTE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SWSTE.2016.11","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
This paper focuses on a software framework to support face recognition, a specific area of image processing. For the processing approach, we use principal component analysis (PCA), a data dimensionality reduction approach. The goal of this study is to understand the entire face recognition process with PCA and to present a software framework supporting multiple variations, which can be used to help users create customized face recognition applications efficiently.