The biometric systems face variability, incompleteness and insufficiency in data, which affects the performance of the recognition system. In iris recognition systems, several conditions cause different types of degradations on iris data such as the poor quality of the acquired pictures, the iris region which can be partially occluded due to light spots, or by lenses, eyeglasses, hair or eyelids, and adverse illuminations or contrasts. All of these limitations are open problems in the iris recognition and affect the performance of iris localization, iris feature extraction or decision making process, and appear as imperfections in the extracted signature. This paper addresses the use of the uncertainty theory for modeling iris system imperfections. Several comparative experiments were conducted on three subsets, namely CASIA.Ver4: synthetic, thousand and interval iris databases. Experimental results show that our proposed system, based on the possibility theory, improves the iris recognition system in terms ROC, AUC, FAR, FRR and PIN, compared to other iris identification systems.
['Majd Bellaaj', 'Jihen Frikha Elleuch', 'Dorra Sellami', 'Imen Khanfir Kallel']
An Improved Iris Recognition System Based on Possibilistic Modeling