{"title":"Neural network face recognition using statistical feature extraction","authors":"S. El-Khamy, O. Abdel-Alim, M. Saii","doi":"10.1109/NRSC.2000.838960","DOIUrl":null,"url":null,"abstract":"Recognition method of human face using statistical analysis feature extraction and a neural network algorithm is proposed. In the preprocessing step we detect the edges of the face image by using the Sobel algorithm. Then we propose a new method to transform the two-dimension black and white image to a one-dimension vector. Finally, based on the statistical analysis, we extract seven features. In the recognition step we use the fast backpropagation (FBP) algorithm. Computer simulation results with 100 test images of 10 persons (the images of each person in a various pauses, facial expression, and facial details) show that the proposed method yields a high recognition rate.","PeriodicalId":211510,"journal":{"name":"Proceedings of the Seventeenth National Radio Science Conference. 17th NRSC'2000 (IEEE Cat. No.00EX396)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2000-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Seventeenth National Radio Science Conference. 17th NRSC'2000 (IEEE Cat. No.00EX396)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NRSC.2000.838960","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
Recognition method of human face using statistical analysis feature extraction and a neural network algorithm is proposed. In the preprocessing step we detect the edges of the face image by using the Sobel algorithm. Then we propose a new method to transform the two-dimension black and white image to a one-dimension vector. Finally, based on the statistical analysis, we extract seven features. In the recognition step we use the fast backpropagation (FBP) algorithm. Computer simulation results with 100 test images of 10 persons (the images of each person in a various pauses, facial expression, and facial details) show that the proposed method yields a high recognition rate.