Human iris feature extraction under pupil size variation using local texture descriptors

Abstract

The human iris texture is one of the most reliable biometric traits because it is unique, and the iris pattern remains stable for years. However, iris images acquired under uncontrolled illumination is one source of difficulties for iris recognition systems, mainly in applications at a distance and in non-cooperative environments. Different levels of light cause iris texture modifications due to pupil size variation. The iris contains 02 groups of muscles: the sphincter pupillae and the dilator pupillae. When the sphincter pupillae contracts the iris reduces the size of the pupil and its texture changes. It is well known in the biometric literature that pupil dilation degrades iris biometric performance. We propose in this paper to evaluate some local texture descriptors for iris recognition, considering pupil contraction and dilation. Furthermore, we propose 02 new texture descriptors called Median-Local-Mapped-Pattern (Median-LMP) and Modified Median-Local-Mapped-Pattern (MM-LMP) and compare their performances to the original Local Mapped Pattern (LMP), the Completed Modeling of Local Binary Pattern (CLBP), the Median Binary Pattern (MBP), the Weber Local Descriptor (WLD) and the Daugman’s method. Our results show that our methodology is more robust when we compare iris samples with different levels of pupil sizes (dilated vs contracted). Besides this, our descriptor performs better than all the compared methods, primarily if one iris with a contracted pupil is used for searching another iris with a dilated pupil.