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• W2944477687 abstract "Fingerprint Indexing allows filtering databases for samples most similar to a query fingerprint. Fixed-length feature vectors invariant to the count of minutiae allow fast comparison. This is the first approach, which uses Deep Convolutional Neural Networks to generate fixed-length index vectors from minutia neighbourhoods. It is also invariant to any sorting of minutiae with respect to their vicinity. No fingerprint image data is required. Usage of only standardized ISO/IEC minutia templates allows even to deploy to legacy systems. Our approach is therefore called Deep ISO Minutiae Indexing (DIMI). Index vectors can be as short as 16 float values per sample. DIMI was evaluated at the independent benchmark framework FVCongoing. In the incremental search scenario, DIMI achieves an average penetration ratio of 0.747% for test FIDX-50K and 0.723% for test FIDX-10K respectively. These are the best results reported so far." @default.
• W2944477687 created "2019-05-16" @default.
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• W2944477687 date "2018-11-01" @default.
• W2944477687 modified "2023-10-14" @default.
• W2944477687 title "Learning Neighbourhoods for Fingerprint Indexing" @default.
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• W2944477687 doi "https://doi.org/10.1109/sitis.2018.00018" @default.
• W2944477687 hasPublicationYear "2018" @default.
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