NDLI: A new probabilistic Iris Quality Measure for comprehensive noise detection

Content Provider	IEEE Xplore Digital Library
Author	Krichen, E. Garcia-Salicetti, S. Dorizzi, B.
Copyright Year	2007
Description	Author affiliation: Inst. Nat. des Telecommun, Evry (Krichen, E.; Garcia-Salicetti, S.; Dorizzi, B.)
Abstract	In this article, we present a novel probabilistic iris quality measure based on a Gaussian mixture model (GMM). We compare its behavior to that of other standard iris quality metrics on two different types of noise which can corrupt the iris texture: occlusions and blurring. In the case of occlusions, we compare our GMM-based quality measure to an active contour method for eyelids and eyelashes detection. Finally, in the case of iris blurring, we compare our quality measure to a standard method based on Fourier transform and wavelets. For the latter, we have developed a new method to detect blur suitable for iris images. In our tests, we have used the ICE 2005 database and OSIRIS, an iris reference system based on the classical approach proposed by Daugman and developed in the framework of BioSecure European Network of Excellence for comparative evaluation purposes. Experiments show a significant improvement of performance when our GMM- based quality measure is used instead of the classical methods above mentioned. In particular, results show that this probabilistic quality measure based on a GMM trained on good quality images is independent from the kind of noise involved.
Starting Page	1
Ending Page	6
File Size	2652633
Page Count	6
File Format	PDF
ISBN	9781424415960
DOI	10.1109/BTAS.2007.4401906
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Iris System testing Fourier transforms Image databases Eyelashes Measurement standards Eyelids Ice Noise measurement Active contours
Content Type	Text
Resource Type	Article

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