George D. C. Cavalcanti, Rodrigo C. Doria, E. C. B. C. Filho
{"title":"不同大小签名离线识别的特征选择","authors":"George D. C. Cavalcanti, Rodrigo C. Doria, E. C. B. C. Filho","doi":"10.1109/NNSP.2002.1030047","DOIUrl":null,"url":null,"abstract":"The aim of this work is to select a set of features, which have good performance to solve the problem of signature recognition of different sizes. The signature database was formed for three sizes of signatures per user, small, median and big. This study uses structural features, pseudo-dynamic features and five moment groups. The feature selection method chosen is the one that select the best individual features based on classifiers like Bayes and k-NN.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Feature selection for off-line recognition of different size signatures\",\"authors\":\"George D. C. Cavalcanti, Rodrigo C. Doria, E. C. B. C. Filho\",\"doi\":\"10.1109/NNSP.2002.1030047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this work is to select a set of features, which have good performance to solve the problem of signature recognition of different sizes. The signature database was formed for three sizes of signatures per user, small, median and big. This study uses structural features, pseudo-dynamic features and five moment groups. The feature selection method chosen is the one that select the best individual features based on classifiers like Bayes and k-NN.\",\"PeriodicalId\":117945,\"journal\":{\"name\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.2002.1030047\",\"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 the 12th IEEE Workshop on Neural Networks for Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.2002.1030047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature selection for off-line recognition of different size signatures
The aim of this work is to select a set of features, which have good performance to solve the problem of signature recognition of different sizes. The signature database was formed for three sizes of signatures per user, small, median and big. This study uses structural features, pseudo-dynamic features and five moment groups. The feature selection method chosen is the one that select the best individual features based on classifiers like Bayes and k-NN.
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