{"title":"基于识别性能的盲签名和视觉签名数据采集协议特征分析","authors":"Rehab Ibrahem, Meryem Erbilek","doi":"10.1109/CICN.2017.8319370","DOIUrl":null,"url":null,"abstract":"In this paper, we analyse the differences and similarities of features in the context of blind and visual signing data collection protocols with respect to the signature biometrics identification performance. As a result of this performed experimental analysis, powerful features which maximises system accuracy while minimising the performance differential across different signature data collection protocols (visual and blind signing) is extensively tested and documented.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature analysis of blind and visual signature data collection protocols based on the identification performance\",\"authors\":\"Rehab Ibrahem, Meryem Erbilek\",\"doi\":\"10.1109/CICN.2017.8319370\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we analyse the differences and similarities of features in the context of blind and visual signing data collection protocols with respect to the signature biometrics identification performance. As a result of this performed experimental analysis, powerful features which maximises system accuracy while minimising the performance differential across different signature data collection protocols (visual and blind signing) is extensively tested and documented.\",\"PeriodicalId\":339750,\"journal\":{\"name\":\"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICN.2017.8319370\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICN.2017.8319370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature analysis of blind and visual signature data collection protocols based on the identification performance
In this paper, we analyse the differences and similarities of features in the context of blind and visual signing data collection protocols with respect to the signature biometrics identification performance. As a result of this performed experimental analysis, powerful features which maximises system accuracy while minimising the performance differential across different signature data collection protocols (visual and blind signing) is extensively tested and documented.