Automatic localization of pupil using eccentricity and iris using gradient based method

Detection and matching of iris region in images taken in Unfavorable conditions like light, camera distance etc. For making iris recognition systems more effective

“…After this, iris segmentation is a critical co mponent of any iris detection system because inaccuracies in localizing the iris can severely degrade the system's matching accuracy and undermine the system's usefulness. A usual iris segmentation process includes the following steps: demarcating the iris inner and outer boundaries at the pupil and sclera; demarcating its upper and/or lower eyelids if they occlude; and detecting and excluding any superimposed occlusions such as eyelashes, eyelids, shadows, or reflections [18]. The segmentation module detects the papillary and limbos boundaries and identifies the regions where the eyelids and eyelashes interrupt the limbos boundary's contour.…”

Reliable iris localization using integral projection function and 2D-shape properties

“…Among these traits, the face, fingerprints, and voice have long been used for human recognition. However, such traits change as an individual ages and can be artificially duplicated as well [2] . On the other hand, the iris is a unique, stable, and non-invasive biometric.…”

“…The iris pattern remains stable over the entire life of a subject, except for some minor changes that occur in the early stages of life after birth [3,4] . A typical iris recognition system includes eye image capture, iris segmentation, feature extraction, and matching [2,3,5] . The term segmentation generally refers to the extraction of features from an image [6,7] .…”

“…Iris segmentation plays a critical role in the overall recognition accuracy because the accuracy of the other modules also depends on its performance. The iris segmentation module localizes the pupillary (iris inner boundary/pupil circle) and limbic (iris outer boundary/iris circle) boundaries and then detects and excludes any other superimposed noises, such as the eyelids, eyelashes, and/or specular reflections [1][2][3]5] . Researchers have proposed different methods for iris segmentation, including histogram-and thresholding-based techniques [2,5] , gradients and edge detectors [3,4] , and genetic algorithms and fuzzy logic [8,9] , among others.…”

“…[1,2,5]) because iris localization is considered to be the most challenging and time-consuming system module compared with other system modules. Daugman [3] used an integrodifferential operator (IDO) to localize the iris in an eye image.…”