{"title":"基于人工神经的人脸重复识别","authors":"S. R. C. Nursari, Rizki Rahmatunisa","doi":"10.32877/bt.v6i1.899","DOIUrl":null,"url":null,"abstract":"The facial recognition system develops a basic identity verification system based on the natural features of human faces. The study included duplicate passport identification, which checks each person's facial accuracy through a sample of facial data. The data used in this study were 180 face samples at the training stage and 30 face samples at the testing stage. The face sample taken is a forward-facing face that is not obstructed by an object. Face image recognition in this study combines GLCM method, color moment, shape extraction and backpropagation algorithm. The test process recognition rate is 78.83%.","PeriodicalId":405015,"journal":{"name":"bit-Tech","volume":"237 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Face Duplication Identifier Using Artificial Nerves\",\"authors\":\"S. R. C. Nursari, Rizki Rahmatunisa\",\"doi\":\"10.32877/bt.v6i1.899\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The facial recognition system develops a basic identity verification system based on the natural features of human faces. The study included duplicate passport identification, which checks each person's facial accuracy through a sample of facial data. The data used in this study were 180 face samples at the training stage and 30 face samples at the testing stage. The face sample taken is a forward-facing face that is not obstructed by an object. Face image recognition in this study combines GLCM method, color moment, shape extraction and backpropagation algorithm. The test process recognition rate is 78.83%.\",\"PeriodicalId\":405015,\"journal\":{\"name\":\"bit-Tech\",\"volume\":\"237 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"bit-Tech\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32877/bt.v6i1.899\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"bit-Tech","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32877/bt.v6i1.899","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Face Duplication Identifier Using Artificial Nerves
The facial recognition system develops a basic identity verification system based on the natural features of human faces. The study included duplicate passport identification, which checks each person's facial accuracy through a sample of facial data. The data used in this study were 180 face samples at the training stage and 30 face samples at the testing stage. The face sample taken is a forward-facing face that is not obstructed by an object. Face image recognition in this study combines GLCM method, color moment, shape extraction and backpropagation algorithm. The test process recognition rate is 78.83%.