Abstract
This paper proposes an efficient indexing technique for fingerprint database using minutiae based geometric hashing. A fixed length feature vector built from each minutia, known as Minutia Binary Pattern, has been suggested for the accurate match at the time of searching. Unlike existing geometric based indexing techniques, the proposed technique inserts each minutia along with the feature vector exactly once into a hash table. As a result, it reduces both computational and memory costs. Since minutiae of all fingerprint images in the database are found to be well distributed into the hash table, no rehashing is required. Experiments over FVC 2004 datasets prove the superiority of the proposed indexing technique against well known geometric based indexing techniques using fingerprints.
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Umarani, J., Viswanathan, J., Gupta, A.K., Gupta, P. (2012). Minutiae Based Geometric Hashing for Fingerprint Database. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_61
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DOI: https://doi.org/10.1007/978-3-642-31837-5_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31836-8
Online ISBN: 978-3-642-31837-5
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