Adaptive Segmentation for Unconstrained Iris Recognition

In standard iris recognition systems, a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture, look-and-stare constraints, and a close distance requirement to the capture device. When these conditions are relaxed, the system’s performance significantly deteriorates due to segmentation and feature extraction problems. Herein, a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments. First, the algorithm scans the whole iris image in the Hue Saturation Value (HSV) color space for local maxima to detect the sclera region. The image quality is then assessed by computing global features in red, green and blue (RGB) space, as noisy images have heterogeneous characteristics. The iris images are accordingly classified into seven categories based on their global RGB intensities. After the classification process, the images are filtered, and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring. Finally, to characterize the pupil area, the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring. The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.