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Fingerprint recognition system based on modified multi-connect architecture (MMCA)
Affiliation:1. Department of Internal Medicine, Ghent University, Ghent, Belgium;2. Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium;3. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, United Kingdom;4. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium;5. Department of Computer Science and Artificial Intelligence of the University of Granada, 18071 Granada, Spain;6. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Abstract:Fingerprinting is the most widely used and recognised biometric technology for human authentication. Fingerprint authentication has a proven record as highly secure and convenient as compared to passwords. Hence, fingerprint sensing has come to berecognized as a common and product-differentiating feature in smartphones, tablets and PCs. This paper proposes to develop a Fingerprint recognition system for authentication of personsby using a new technique, termed as ‘associative memory with modify multi-connect architecture’. This, in turn, may pave the way to develop more efficient Fingerprint systemshaving accuracy and lesserprocessing time. Further, with application of additional tranches of associative memory, such systems in the future will acquirepotential to perform highly complex operations and save memory. In this paper, three databases viz., FVC (2004) database, internal database and International NIST database 4 are used. FVC (2004) database contains 640 fingerprint patterns, while internal database contains 2500 different fingerprint patterns; and the International NIST database 4 consists of 2000 pairs of fingerprint patterns. The proposed fingerprint recognition system has an average accuracy of 99.56% and a pattern recognition processing time of approximately 30 s.
Keywords:Associative memory  Biometric and fingerprint
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