Abstract
Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. The iris segmentation is a process of detecting the pupil, iris’s outer boundary, and eyelids in the iris image. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR) illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and occlusions by the eyelids, eyelashes, and eyebrow hair. In the proposed method, first, a novel edge-map is created from the iris image, which is based on combining the conventional thresholding and edge detection based segmentation techniques, and then, the general circular Hough transform (CHT) is used to find the pupil circle parameters in the edge-map. Our main contribution in this research is a novel edge-map creation technique, which reduces the false edges drastically in the edge-map of the iris image and makes the pupil localization in the noisy NIR images more accurate, fast, robust, and simple. The proposed method was tested with three iris databases: CASIA-Iris-Thousand (version 4.0), CASIA-Iris-Lamp (version 3.0), and MMU (version 2.0). The average accuracy of the proposed method is 99.72% and average time cost per image is 0.727 sec.
Highlights
Iris recognition [1,2,3] is one of the most accurate and secured methods of identifying persons among all the available biometric identification techniques
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 Chinese Academy of Sciences’ Institute of Automation (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 CASIAIris-Thousand, version 4.0 (CITHV4), database images [6]
For the accurate iris segmentation in the NIR images, a high pupil localization accuracy is required because if the pupil was wrongly detected in the image, the iris’s outer boundary would be, as it requires pupil circle parameters as input for the detection [7, 8]
Summary
Iris recognition [1,2,3] is one of the most accurate and secured methods of identifying persons among all the available biometric identification techniques. 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]. We propose a novel edge-map creation technique for the noisy NIR images that reduces the false edges drastically so that the pupil can be localized accurately and rapidly using a general circular Hough transform (CHT) algorithm. (1) In the NIR images, if the pupil were wrongly localized, the iris’s outer boundary would be, as the iris’s outer boundary localization methods use the pupil circle parameters as inputs [7, 8]. The remainder of the paper is organized as follows: Section 2 discusses related work and Section 3 explains the proposed pupil localization method and its implementation.
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