Abstract
This paper proposes a novel fingerprint identification system using minutiae neighborhood structure. First, we construct the nearest neighborhood for each minutia in the fingerprint. In the next step, we extract the features such as rotation invariant distances and orientation differences from the neighborhood structure. Then, we use these features to compute the index keys for each fingerprint. During identification of a query, a nearest neighbor algorithm is used to retrieve the best matches. Further, this approach enrolls the new fingerprints dynamically. This approach has been experimented on different benchmark Fingerprint Verification Competition (FVC) databases and the results are promising.
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Kavati, I., Kiran Kumar, G., Srinivas Rao, K. (2018). Fast Fingerprint Retrieval Using Minutiae Neighbor Structure. In: Reddy Edla, D., Lingras, P., Venkatanareshbabu K. (eds) Advances in Machine Learning and Data Science. Advances in Intelligent Systems and Computing, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-10-8569-7_20
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DOI: https://doi.org/10.1007/978-981-10-8569-7_20
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