R. Telgad, Almas M. N. Siddiqui, Savita A. Lothe, P. Deshmukh, Gajanan Jadhao
{"title":"基于虹膜、指纹、人脸的高效安全生物识别系统的开发","authors":"R. Telgad, Almas M. N. Siddiqui, Savita A. Lothe, P. Deshmukh, Gajanan Jadhao","doi":"10.1109/ICISIM.2017.8122156","DOIUrl":null,"url":null,"abstract":"In this research paper three biometric characteristics are used i.e. Fingerprint, Face, Iris at score level of Fusion. For finger print images two methods are used i.e. Minutiae Extraction and Gabor filter approach. For Iris recognition system Gabor wavelet is used for feature selection. For Face biometric system P.C.A. is used for feature selection. The match count of every trait is calculated. Then the generated result of match and non match is utilized for the sum score level fusion. Then decision is find out for persons recognition. The system is tested on std. Dataset and KVK data set. On KVK dataset it generates an the results as 99.7 % with FAR of 0.02% and FRR of 0.1% and for FVC 2004 dataset and MMU dataset it gives the result as 99.8 % with FAR of 0.11% and FRR of 0.09%","PeriodicalId":139000,"journal":{"name":"2017 1st International Conference on Intelligent Systems and Information Management (ICISIM)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Development of an efficient secure biometric system by using iris, fingerprint, face\",\"authors\":\"R. Telgad, Almas M. N. Siddiqui, Savita A. Lothe, P. Deshmukh, Gajanan Jadhao\",\"doi\":\"10.1109/ICISIM.2017.8122156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this research paper three biometric characteristics are used i.e. Fingerprint, Face, Iris at score level of Fusion. For finger print images two methods are used i.e. Minutiae Extraction and Gabor filter approach. For Iris recognition system Gabor wavelet is used for feature selection. For Face biometric system P.C.A. is used for feature selection. The match count of every trait is calculated. Then the generated result of match and non match is utilized for the sum score level fusion. Then decision is find out for persons recognition. The system is tested on std. Dataset and KVK data set. On KVK dataset it generates an the results as 99.7 % with FAR of 0.02% and FRR of 0.1% and for FVC 2004 dataset and MMU dataset it gives the result as 99.8 % with FAR of 0.11% and FRR of 0.09%\",\"PeriodicalId\":139000,\"journal\":{\"name\":\"2017 1st International Conference on Intelligent Systems and Information Management (ICISIM)\",\"volume\":\"97 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 1st International Conference on Intelligent Systems and Information Management (ICISIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISIM.2017.8122156\",\"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 1st International Conference on Intelligent Systems and Information Management (ICISIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISIM.2017.8122156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of an efficient secure biometric system by using iris, fingerprint, face
In this research paper three biometric characteristics are used i.e. Fingerprint, Face, Iris at score level of Fusion. For finger print images two methods are used i.e. Minutiae Extraction and Gabor filter approach. For Iris recognition system Gabor wavelet is used for feature selection. For Face biometric system P.C.A. is used for feature selection. The match count of every trait is calculated. Then the generated result of match and non match is utilized for the sum score level fusion. Then decision is find out for persons recognition. The system is tested on std. Dataset and KVK data set. On KVK dataset it generates an the results as 99.7 % with FAR of 0.02% and FRR of 0.1% and for FVC 2004 dataset and MMU dataset it gives the result as 99.8 % with FAR of 0.11% and FRR of 0.09%