Abstract
Any well known fingerprint matching algorithm cannot provide 100% accuracy for all databases. One should explore the possibility of fusion of multi-algorithms to achieve better performance on such databases. One of the major challenges is to design a fusion strategy which is both adaptive and improving with respect to the candidate database. This paper proposes an adaptive ensemble using statistical properties of two well known state-of-the-art minutiae based fingerprint matching algorithms to achieve (1) improvement on fingerprint recognition benchmark, (2) outperform on multiple databases. Experiments have been conducted on two databases containing multiple fingerprint impressions of 140 and 500 users. One of them is widely used publicly available databases and another one is our in-house database. Experimental results have shown the significant gain in performance.
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Tiwari, K., Kaushik, V.D., Gupta, P. (2016). An Adaptive Multi-algorithm Ensemble for Fingerprint Matching. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_6
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DOI: https://doi.org/10.1007/978-3-319-42291-6_6
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