{"title":"风格签名对抗文体学中的生物识别动物园","authors":"Kalaivani Sundararajan, T. Neal, D. Woodard","doi":"10.1109/ICB2018.2018.00047","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate the challenges of using a person's writing style as a cognitive biometric modality by applying Doddington's idea of Biometric menagerie. To the best of our knowledge, biometric menagerie analysis has been on performed on a cognitive biometric modality for the first time. The presence of goats, wolves and lambs in this modality is demonstrated using two publicly available datasets - Blogs and IMDB1M. To combat this challenging problem, we further propose using person-specific features referred to as \"Style signatures\" which may be better at distinguishing different individuals. Experimental results show that using person-specific Style signatures improve verification by 3.6-5.5% on both datasets.","PeriodicalId":130957,"journal":{"name":"2018 International Conference on Biometrics (ICB)","volume":"275 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Style Signatures to Combat Biometric Menagerie in Stylometry\",\"authors\":\"Kalaivani Sundararajan, T. Neal, D. Woodard\",\"doi\":\"10.1109/ICB2018.2018.00047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we investigate the challenges of using a person's writing style as a cognitive biometric modality by applying Doddington's idea of Biometric menagerie. To the best of our knowledge, biometric menagerie analysis has been on performed on a cognitive biometric modality for the first time. The presence of goats, wolves and lambs in this modality is demonstrated using two publicly available datasets - Blogs and IMDB1M. To combat this challenging problem, we further propose using person-specific features referred to as \\\"Style signatures\\\" which may be better at distinguishing different individuals. Experimental results show that using person-specific Style signatures improve verification by 3.6-5.5% on both datasets.\",\"PeriodicalId\":130957,\"journal\":{\"name\":\"2018 International Conference on Biometrics (ICB)\",\"volume\":\"275 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Biometrics (ICB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICB2018.2018.00047\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Biometrics (ICB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICB2018.2018.00047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Style Signatures to Combat Biometric Menagerie in Stylometry
In this paper, we investigate the challenges of using a person's writing style as a cognitive biometric modality by applying Doddington's idea of Biometric menagerie. To the best of our knowledge, biometric menagerie analysis has been on performed on a cognitive biometric modality for the first time. The presence of goats, wolves and lambs in this modality is demonstrated using two publicly available datasets - Blogs and IMDB1M. To combat this challenging problem, we further propose using person-specific features referred to as "Style signatures" which may be better at distinguishing different individuals. Experimental results show that using person-specific Style signatures improve verification by 3.6-5.5% on both datasets.