{"title":"基于神经网络的指纹识别","authors":"W. Leung, S. Leung, W. H. Lau, A. Luk","doi":"10.1109/NNSP.1991.239519","DOIUrl":null,"url":null,"abstract":"The authors describe a neural network based approach for automated fingerprint recognition. Minutiae are extracted from the fingerprint image via a multilayer perceptron (MLP) classifier with one hidden layer. The backpropagation learning technique is used for its training. Selected features are represented in a special way such that they are simultaneously invariant under shift, rotation and scaling. Simulation results are obtained with good detection ratio and low failure rate. The proposed method is found to be reliable for a system with a small set of fingerprint data.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"69","resultStr":"{\"title\":\"Fingerprint recognition using neural network\",\"authors\":\"W. Leung, S. Leung, W. H. Lau, A. Luk\",\"doi\":\"10.1109/NNSP.1991.239519\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors describe a neural network based approach for automated fingerprint recognition. Minutiae are extracted from the fingerprint image via a multilayer perceptron (MLP) classifier with one hidden layer. The backpropagation learning technique is used for its training. Selected features are represented in a special way such that they are simultaneously invariant under shift, rotation and scaling. Simulation results are obtained with good detection ratio and low failure rate. The proposed method is found to be reliable for a system with a small set of fingerprint data.<<ETX>>\",\"PeriodicalId\":354832,\"journal\":{\"name\":\"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"69\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.1991.239519\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.1991.239519","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The authors describe a neural network based approach for automated fingerprint recognition. Minutiae are extracted from the fingerprint image via a multilayer perceptron (MLP) classifier with one hidden layer. The backpropagation learning technique is used for its training. Selected features are represented in a special way such that they are simultaneously invariant under shift, rotation and scaling. Simulation results are obtained with good detection ratio and low failure rate. The proposed method is found to be reliable for a system with a small set of fingerprint data.<>