{"title":"基于神经网络的潜在指纹分类","authors":"Hamid Jan, Amjad Ali","doi":"10.22364/bjmc.2018.6.1.03","DOIUrl":null,"url":null,"abstract":"To classify latent fingerprint images by using papillary patterns, the proposed method applies the Gabor filter, Haar and Daubechies wavelet transformations, and a multilevel neural network. Numerical experiments were performed, and the results of the proposed solution were compared. The results have shown that we can effectively classify latent fingerprints by applying in combination the Gabor filter, Daubechies wavelet transform of the fifth level, and a neural network.","PeriodicalId":431209,"journal":{"name":"Balt. J. Mod. Comput.","volume":"600 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Classification of Latent Fingerprints Using Neural Networks\",\"authors\":\"Hamid Jan, Amjad Ali\",\"doi\":\"10.22364/bjmc.2018.6.1.03\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To classify latent fingerprint images by using papillary patterns, the proposed method applies the Gabor filter, Haar and Daubechies wavelet transformations, and a multilevel neural network. Numerical experiments were performed, and the results of the proposed solution were compared. The results have shown that we can effectively classify latent fingerprints by applying in combination the Gabor filter, Daubechies wavelet transform of the fifth level, and a neural network.\",\"PeriodicalId\":431209,\"journal\":{\"name\":\"Balt. J. Mod. Comput.\",\"volume\":\"600 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Balt. J. Mod. Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22364/bjmc.2018.6.1.03\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Balt. J. Mod. Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22364/bjmc.2018.6.1.03","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of Latent Fingerprints Using Neural Networks
To classify latent fingerprint images by using papillary patterns, the proposed method applies the Gabor filter, Haar and Daubechies wavelet transformations, and a multilevel neural network. Numerical experiments were performed, and the results of the proposed solution were compared. The results have shown that we can effectively classify latent fingerprints by applying in combination the Gabor filter, Daubechies wavelet transform of the fifth level, and a neural network.