Uncertainty Theories Based Iris Recognition System
Bellaaj, Majd (University of Sfax (Sfax, Tunísia). National Engineering School of Sfax)
Khanfir Kallel, Imen (University of Sfax (Sfax, Tunísia). National Engineering School of Sfax)
Sellami, Dorra (University of Sfax (Sfax, Tunísia). National Engineering School of Sfax)

Data:	2017
Resum:	The performance and robustness of the iris-based recognition systems still suffer from imperfection in the biometric information. This paper makes an attempt to address these imperfections and deals with important problem for real system. We proposed a new method for iris recognition system based on uncertainty theories to treat imperfection iris feature. Several factors cause different types of degradation in iris data such as the poor quality of the acquired pictures, the partial occlusion of the iris region due to light spots, or lenses, eyeglasses, hair or eyelids, and adverse illumination and/or contrast. All of these factors are open problems in the field of iris recognition and affect the performance of iris segmentation, its feature extraction or decision making process, and appear as imperfections in the extracted iris feature. The aim of our experiments is to model the variability and ambiguity in the iris data with the uncertainty theories. This paper illustrates the importance of the use of this theory for modeling or/and treating encountered imperfections. Several comparative experiments are conducted on two subsets of the CASIA-V4 iris image database namely Interval and Synthetic. Compared to a typical iris recognition system relying on the uncertainty theories, experimental results show that our proposed model improves the iris recognition system in terms of Equal Error Rates (EER), Area Under the receiver operating characteristics Curve (AUC) and Accuracy Recognition Rate (ARR) statistics.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Iris Recognition System ; Iris Feature Imperfections ; Probability Theory ; Possibility Theory ; Biometrics ; Biometric technologies ; Pattern recognition
Publicat a:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 16 Núm. 2 (2017) , p. 29-32 (Special Issue on Recent PhD Thesis Dissemination (2017)) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v16-n2-majd
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/v16-n2-majd
DOI: 10.5565/rev/elcvia.1131

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