文章基本信息

标题：Multifinger Feature Level Fusion Based Fingerprint Identification
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作者：Praveen N ; Tessamma Thomas
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2012
卷号：3
期号：11
DOI：10.14569/IJACSA.2012.031114
出版社：Science and Information Society (SAI)
摘要：Fingerprint based authentication systems are one of the cost-effective biometric authentication techniques employed for personal identification. As the data base population increases, fast identification/recognition algorithms are required with high accuracy. Accuracy can be increased using multimodal evidences collected by multiple biometric traits. In this work, consecutive fingerprint images are taken, global singularities are located using directional field strength and their local orientation vector is formulated with respect to the base line of the finger. Feature level fusion is carried out and a 32 element feature template is obtained. A matching score is formulated for the identification and 100% accuracy was obtained for a database of 300 persons. The polygonal feature vector helps to reduce the size of the feature database from the present 70-100 minutiae features to just 32 features and also a lower matching threshold can be fixed compared to single finger based identification.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; fingerprint; multimodal biometrics; gradient; orientation field; singularity; matching score.