Probability distribution function based iris recognition boosted by the mean rule

TitreProbability distribution function based iris recognition boosted by the mean rule
Type de publicationCommunication
TypeCommunication avec actes dans un congrès
Année2015
LangueAnglais
Date du colloque17-18/01/2015
Titre du colloque2014 International Conference on Intelligent Computing and Internet of Things (ICIT)
Titre des actes ou de la revueProceedings of 2015 International Conference on Intelligent Computing and Internet of Things
Pagination47-50
AuteurPjatkin, Kert , Daneshmand, Morteza , Rasti, Pejman , Anbarjafari, Gholamreza
PaysChine
EditeurIEEE
VilleHarbin
ISBN978-1-4799-7534-1
Mots-clésclassification, Iris recognition, Kullback-Leibler divergence, Mean rule, Probability distribution function
Résumé en anglais

In this work, a new iris recognition algorithm based on tonal distribution of iris images is introduced. During the process of identification probability distribution functions of colored irises are generated in HSI and YCbCr color spaces. The discrimination between classes is obtained by using Kullback-Leibler divergence. In order to obtain the final decision on recognition, the multi decision on various color channels has been combined by employing mean rule. The decisions of H, S, Y, Cb and Cr color channels have been combined. The proposed technique overcome the conventional principle component analysis technique and achieved a recognition rate of 100% using the UPOL database. The major advantage is the fact that it is computationally less complex than the Daugman's algorithm and it is suitable for using visible light camera as opposed to the one proposed by Daugman where NIR cameras are used for obtaining the irises.

URL de la noticehttp://okina.univ-angers.fr/publications/ua17516
DOI10.1109/ICAIOT.2015.7111535
Lien vers le document en ligne

https://ieeexplore.ieee.org/document/7111535/