{"title":"改进的指纹码匹配功能","authors":"Gustavo de Sa, R. Lotufo","doi":"10.1109/SIBGRAPI.2006.25","DOIUrl":null,"url":null,"abstract":"FingerCode is a fingerprint correlation matching scheme that relies on texture information. In this scheme, the oriented components are extracted from a fingerprint image using a bank of Gabor filters, and a directional texture feature vector is computed for each oriented component. The feature vectors from the input and template images are compared and a matching score is obtained. Here, we explore ways to improve the matching score for the FingerCode method by using more complex matching functions. The best results were obtained by applying a nonlinear function to the texture values and weighting the texture vectors based on the spatial distribution","PeriodicalId":253871,"journal":{"name":"2006 19th Brazilian Symposium on Computer Graphics and Image Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Improved FingerCode Matching Function\",\"authors\":\"Gustavo de Sa, R. Lotufo\",\"doi\":\"10.1109/SIBGRAPI.2006.25\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"FingerCode is a fingerprint correlation matching scheme that relies on texture information. In this scheme, the oriented components are extracted from a fingerprint image using a bank of Gabor filters, and a directional texture feature vector is computed for each oriented component. The feature vectors from the input and template images are compared and a matching score is obtained. Here, we explore ways to improve the matching score for the FingerCode method by using more complex matching functions. The best results were obtained by applying a nonlinear function to the texture values and weighting the texture vectors based on the spatial distribution\",\"PeriodicalId\":253871,\"journal\":{\"name\":\"2006 19th Brazilian Symposium on Computer Graphics and Image Processing\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 19th Brazilian Symposium on Computer Graphics and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIBGRAPI.2006.25\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 19th Brazilian Symposium on Computer Graphics and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIBGRAPI.2006.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
FingerCode is a fingerprint correlation matching scheme that relies on texture information. In this scheme, the oriented components are extracted from a fingerprint image using a bank of Gabor filters, and a directional texture feature vector is computed for each oriented component. The feature vectors from the input and template images are compared and a matching score is obtained. Here, we explore ways to improve the matching score for the FingerCode method by using more complex matching functions. The best results were obtained by applying a nonlinear function to the texture values and weighting the texture vectors based on the spatial distribution