{"title":"人脸识别的增强判别线性回归分类","authors":"Xiaochao Qu, Hyoung-Joong Kim","doi":"10.1109/ISSNIP.2014.6827696","DOIUrl":null,"url":null,"abstract":"Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Enhanced discriminant linear regression classification for face recognition\",\"authors\":\"Xiaochao Qu, Hyoung-Joong Kim\",\"doi\":\"10.1109/ISSNIP.2014.6827696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.\",\"PeriodicalId\":269784,\"journal\":{\"name\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSNIP.2014.6827696\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSNIP.2014.6827696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhanced discriminant linear regression classification for face recognition
Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.