A dynamic non-circular iris localization technique for non-ideal data

A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images

“…Daugman proposed an integrodifferential operator (IDO) that acts as a circular edge detector to localize the iris boundaries, whereas Wildes method uses the Hough transform (HT) to detect the circular edges. However, these iris localization methods work under the controlled environments and their performance strongly deteriorates when dealing with the noisy data [7]. After Daugman and Wildes's methods, the researchers have proposed various iris segmentation methods with the improved accuracy and speed for different databases.…”

“…Some of the state-of-the-art iris segmentation methods along with the main techniques they use are provided in the tabular form in [10], where most of the methods are based on the Hough transform (HT) technique. Some of the recent methods to localize the irises in the noisy NIR and VW images are described in [7], [8], [11][12][13], and [14][15][16], respectively. The HT based techniques consider the iris as a circular ring and the CHT is used to detect the circles as discussed in [8,14,16], but the iris contours may not be the perfect circles in the nonfrontal-view iris images.…”

“…In the iris segmentation of the NIR images, the pupil is generally localized prior to the iris's outer boundary because it is the dark compact region in the image and the pupil boundary is stronger than the iris's outer boundary [7,8]. Having localized the pupil in the image, a subimage around the pupil is processed to detect the iris's outer boundary.…”

“…The iris images from the NIR databases can be categorized into two types: (1) ideal close-up iris images (Figure 1(a)), which are captured in the controlled conditions, such as CASIA-IrisV1 and CASIA-Iris-Interval, version 3.0, database images, and (2) noisy and non-closeup iris images (Figure 1(b)), which are captured from a distance under unconstrained environment, such as CASIA-Iris-Thousand, version 4.0 (CITHV4), database images [6]. The unconstrained or noisy iris images (Figure 1(b)) may have different types of the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and obstructions by eyelids, eyelashes, and eyebrows [7]. One type of nonideal issue that iris images may have is the nonfrontal view, when the user is not looking ahead towards the camera.…”

“…(1) In the NIR images, if the pupil were wrongly localized, the iris's outer boundary would also be, as the iris's outer boundary localization methods use the pupil circle parameters as inputs [7,8]. (2) The time consumed in the pupil localization is much more than the iris's outer boundary localization, as described later in Section 4 of this paper, because the whole eye (iris) image is processed in the pupil localization, whereas a subimage is processed for the iris's outer boundary localization [8,9].…”