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- W4313452989 abstract "The current study examines the impact of discrete wavelet transforms (DWT) and their success rate in the fingerprint images classification. The analyzed fingerprint images belong to the FVC2004 database. Two datasets consist of 80 real images acquired using an optical sensor and 80 synthetic images, respectively, are built. Four discrete wavelet transform families (Coiflets, Symlets, Fejer - Korovkin and Biorthogonal) are implemented for one level of wavelet decomposition. For Low-Low (LL) and High-High (HH) sub-bands, the entropy and energy features are extracted as they contain rich descriptions of the pattern. For each dataset, eight features are extracted, for each image. To reduce the dimensionality of the feature space, the Principal Component Analysis (PCA) is used. The first three principal components (PCs) fed a neural network classifier. The Radial Base Function Neural Network (RBFNN) model efficiently classified the fingerprint images based on PCs obtained for LL frequency level images features, with an accuracy of 88.13% and sensitivity of 95%." @default.
- W4313452989 created "2023-01-06" @default.
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- W4313452989 date "2022-11-17" @default.
- W4313452989 modified "2023-10-06" @default.
- W4313452989 title "Discret Wavelet Fingerprint Features for ANNs Classification Method" @default.
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- W4313452989 doi "https://doi.org/10.1109/ehb55594.2022.9991412" @default.
- W4313452989 hasPublicationYear "2022" @default.
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