{"title":"Fingerprint Verification Using the Texture of Fingerprint Image","authors":"M. Khalil, Dzulkifli Muhammad, Q. Al-Nuzaili","doi":"10.1109/ICMV.2009.18","DOIUrl":null,"url":null,"abstract":"In this paper, a fingerprint verification method is presented that improves matching accuracy by overcoming the shortcomings of previous methods due to missing some minutiae, non-linear distortions, and rotation and distortion variations. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point and then extract a 129 X 129 block, making the reference point its center. From the 4 co-occurrence matrices four statistical descriptors are computed. Experimental results show that the proposed method is more accurate than other methods the average false acceptance rate (FAR) is 0.62%, the average false rejection rate (FRR) is 0.08%, and the equal error rate (EER) is 0.35%.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Second International Conference on Machine Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMV.2009.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
In this paper, a fingerprint verification method is presented that improves matching accuracy by overcoming the shortcomings of previous methods due to missing some minutiae, non-linear distortions, and rotation and distortion variations. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point and then extract a 129 X 129 block, making the reference point its center. From the 4 co-occurrence matrices four statistical descriptors are computed. Experimental results show that the proposed method is more accurate than other methods the average false acceptance rate (FAR) is 0.62%, the average false rejection rate (FRR) is 0.08%, and the equal error rate (EER) is 0.35%.
本文提出了一种指纹验证方法,克服了以往指纹验证方法缺少一些细节、非线性失真以及旋转和失真变化等缺点,提高了匹配精度。该算法通过对指纹图像进行增强,使其准确可靠地确定一个参考点,然后提取一个129 X 129的块,以参考点为中心,从而降低多光谱噪声。从这4个共现矩阵中计算出4个统计描述符。实验结果表明,该方法的平均错误接受率(FAR)为0.62%,平均错误拒绝率(FRR)为0.08%,平均错误率(EER)为0.35%,优于其他方法。