Journal Article10.1016/J.PATREC.2011.08.018
A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform
R. Szewczyk,K. Grabowski,M. Napieralska,Wojciech Sankowski,Mariusz Zubert,Andrzej Napieralski +5 more
TL;DR: This article describes an iris recognition algorithm designed to analyze noisy iris biometric data using visible wavelength images of an eye taken under unconstrained conditions mainly contained in the UBIRIS.v2 database.
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About: This article is published in Pattern Recognition Letters. The article was published on 01 Jun 2012. The article focuses on the topics: Iris recognition & Biometrics.
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The results of the NICE.II Iris biometrics competition
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Ten Lectures on Wavelets.
TL;DR: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal wavelet bases of compactly supported wavelets and multiresolutional analysis.
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Wavelets and filter banks
Gilbert Strang,Truong Q. Nguyen +1 more
- 01 Jan 1996
TL;DR: Wavelet and short-time Fourier analysis is introduced in the context of frequency decompositions, associating wavelet type decompositions with filter banks, and using filter bank theory to construct multiplicity M wavelet frames and tight frames.
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High confidence visual recognition of persons by a test of statistical independence
TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
How iris recognition works
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
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