{"title":"基于频域分析的指纹识别","authors":"Z. H. Khazaal, S. S. Mahdi","doi":"10.1109/SICN47020.2019.9019362","DOIUrl":null,"url":null,"abstract":"This paper concerned with fingerprint authentication based on its class by using effective machine learning such as neural network and Fuzzy C Mean. Gabor filter was used for enhancement fingerprint image. Discrete Wavelet transform was employed as a feature extraction technique in frequency domain. Moreover, Feed Forward Backpropagation Neural network (FFBPNN) and Fuzzy C mean (FCM) were used for classification. To estimate the performance of above two classifiers, a comparison based on recognition efficiency and processing time were obtained. The algorithms were applied on two databases FVC 2004 for 40 persons with eight images per person and another database for 100 persons with ten images per person. The results proved that the FFBPNN is more powerful classification tool in recognition efficiency and execution time. All the programs were executed in MATLAB R 2017b.","PeriodicalId":179575,"journal":{"name":"2019 4th Scientific International Conference Najaf (SICN)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fingerprint Identification based on Frequency Domain Analysis\",\"authors\":\"Z. H. Khazaal, S. S. Mahdi\",\"doi\":\"10.1109/SICN47020.2019.9019362\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper concerned with fingerprint authentication based on its class by using effective machine learning such as neural network and Fuzzy C Mean. Gabor filter was used for enhancement fingerprint image. Discrete Wavelet transform was employed as a feature extraction technique in frequency domain. Moreover, Feed Forward Backpropagation Neural network (FFBPNN) and Fuzzy C mean (FCM) were used for classification. To estimate the performance of above two classifiers, a comparison based on recognition efficiency and processing time were obtained. The algorithms were applied on two databases FVC 2004 for 40 persons with eight images per person and another database for 100 persons with ten images per person. The results proved that the FFBPNN is more powerful classification tool in recognition efficiency and execution time. All the programs were executed in MATLAB R 2017b.\",\"PeriodicalId\":179575,\"journal\":{\"name\":\"2019 4th Scientific International Conference Najaf (SICN)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 4th Scientific International Conference Najaf (SICN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SICN47020.2019.9019362\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 4th Scientific International Conference Najaf (SICN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SICN47020.2019.9019362","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本文利用神经网络和模糊C均值等有效的机器学习方法,研究了基于类的指纹认证问题。采用Gabor滤波对指纹图像进行增强。采用离散小波变换作为频域特征提取技术。采用前馈反向传播神经网络(FFBPNN)和模糊C均值(FCM)进行分类。为了评估这两种分类器的性能,我们基于识别效率和处理时间进行了比较。算法应用于两个数据库FVC 2004(40人)和另一个数据库(100人)(10人)。结果表明,在识别效率和执行时间上,FFBPNN是一种更强大的分类工具。所有程序均在MATLAB R 2017b中执行。
Fingerprint Identification based on Frequency Domain Analysis
This paper concerned with fingerprint authentication based on its class by using effective machine learning such as neural network and Fuzzy C Mean. Gabor filter was used for enhancement fingerprint image. Discrete Wavelet transform was employed as a feature extraction technique in frequency domain. Moreover, Feed Forward Backpropagation Neural network (FFBPNN) and Fuzzy C mean (FCM) were used for classification. To estimate the performance of above two classifiers, a comparison based on recognition efficiency and processing time were obtained. The algorithms were applied on two databases FVC 2004 for 40 persons with eight images per person and another database for 100 persons with ten images per person. The results proved that the FFBPNN is more powerful classification tool in recognition efficiency and execution time. All the programs were executed in MATLAB R 2017b.