Abstract
Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-axis rotations, motion blurs, and non-regular reflections in the eye area. In addition, iris recognition based on visible light environment has been investigated to avoid the use of additional near-infrared (NIR) light camera and NIR illuminator, which increased the difficulty of segmenting the iris region accurately owing to the environmental noise of visible light. To address these issues; this study proposes a two-stage iris segmentation scheme based on convolutional neural network (CNN); which is capable of accurate iris segmentation in severely noisy environments of iris recognition by visible light camera sensor. In the experiment; the noisy iris challenge evaluation part-II (NICE-II) training database (selected from the UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE) dataset were used. Experimental results showed that our method outperformed the existing segmentation methods.
Highlights
Biometrics has two main categories: physiological and behavioral biometrics
We proposed a two-stage iris segmentation method based on convolutional neural networks (CNN), which is capable of robustly finding the true iris boundary in the above-mentioned intense cases with limited user cooperation
The images of the iris were acquired from people walking 4–8 m away from a high-resolution visible light camera with visible light illumination [43]
Summary
Behavioral biometrics considers voice, signature, keystroke, and gait recognition [1], whereas physiological biometrics considers face [2,3], iris [4,5], fingerprints [6], finger vein patterns [7], and palm prints [8]. Most existing iris recognition algorithms are designed for highly controlled cooperative environments, which is the cause of their failure in non-cooperative environments, i.e., those that include noise, off-angles, motion blurs, glasses, hairs, specular reflection (SR), eyelids and eyelashes incorporation, and partially open eyes. The iris is always assumed to be a circular object, and common methods segment it as a circle, but considering intense cases of side view and partially open eyes, the iris boundary deviates from being circular and may include skin, eyelid, and eyelash areas. The accurate segmentation of the iris boundary is important even in intense environments
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