{"title":"计算机对人脸的识别","authors":"Xiao Dunhe, Qian Guohui, Sang Enfang","doi":"10.1109/TENCON.1993.320203","DOIUrl":null,"url":null,"abstract":"Included are two main classes, i.e. frontal face and profile in face recognition. This paper describes the former class and represents a method of feature selection based on psychological test conclusions and K-L transformation to remove the correlativity of the original features, and a new discriminant criteria called Distance Fuzzy Diagnosis Maximum 1-NN, which solves the problem of error decision at the second or third place of order-ranked distance list in Minimum Distance Criteria. Through the identification of individual faces of seventy pictures and ten subject's facial image with different facial orientation, the correct accuracy achieves 97.5% and 90% respectively.<<ETX>>","PeriodicalId":110496,"journal":{"name":"Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The recognition of human faces by computer\",\"authors\":\"Xiao Dunhe, Qian Guohui, Sang Enfang\",\"doi\":\"10.1109/TENCON.1993.320203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Included are two main classes, i.e. frontal face and profile in face recognition. This paper describes the former class and represents a method of feature selection based on psychological test conclusions and K-L transformation to remove the correlativity of the original features, and a new discriminant criteria called Distance Fuzzy Diagnosis Maximum 1-NN, which solves the problem of error decision at the second or third place of order-ranked distance list in Minimum Distance Criteria. Through the identification of individual faces of seventy pictures and ten subject's facial image with different facial orientation, the correct accuracy achieves 97.5% and 90% respectively.<<ETX>>\",\"PeriodicalId\":110496,\"journal\":{\"name\":\"Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TENCON.1993.320203\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of TENCON '93. IEEE Region 10 International Conference on Computers, Communications and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON.1993.320203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Included are two main classes, i.e. frontal face and profile in face recognition. This paper describes the former class and represents a method of feature selection based on psychological test conclusions and K-L transformation to remove the correlativity of the original features, and a new discriminant criteria called Distance Fuzzy Diagnosis Maximum 1-NN, which solves the problem of error decision at the second or third place of order-ranked distance list in Minimum Distance Criteria. Through the identification of individual faces of seventy pictures and ten subject's facial image with different facial orientation, the correct accuracy achieves 97.5% and 90% respectively.<>