Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation. In many cases, iris biometrics acquisition equipment takes a number of images in sequence and combines the segmentation and matching results for each image to strengthen the result. To date, segmentation has occurred in 2D, operating on each image individually. But such methodologies, while powerful, do not take advantage of potential gains in performance afforded by treating sequential images as volumetric data. As a first step in this direction, we apply the Flexible Learning-Free Reconstructoin of Neural Volumes (FLoRIN) framework, an open source segmentation and reconstruction framework originally designed for neural microscopy volumes, to volumetric segmentation of iris videos. Further, we introduce a novel dataset of near-infrared iris videos, in which each subject's pupil rapidly changes size due to visible-light stimuli, as a test bed for FLoRIN. We compare the matching performance for iris masks generated by FLoRIN, deep-learning-based (SegNet), and Daugman's (OSIRIS) iris segmentation approaches. We show that by incorporating volumetric information, FLoRIN achieves a factor of 3.6 to an order of magnitude increase in throughput with only a minor drop in subject matching performance. We also demonstrate that FLoRIN-based iris segmentation maintains this speedup on low-resource hardware, making it suitable for embedded biometrics systems.
More recently, iris segmentation has moved away from these classical approaches toward machine learning oriented methods. Li @cite_6 proposed a method which uses k-means clustering to detect the outer boundary of the iris. In a similar fashion, Sahmoud and Abuhaiba @cite_19 proposed using k-means clustering to determine the region of the iris.
@cite_19 @cite_6
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Recently, iris recognition systems have gained increased attention especially in non-cooperative environments. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction and iris matching steps. Traditional iris segmentation methods provide excellent results when iris images are captured using near infrared cameras under ideal imaging conditions, but the accuracy of these algorithms significantly decreases when the iris images are taken in visible wavelength under non-ideal imaging conditions. In this paper, a new algorithm is proposed to segments iris images captured in visible wavelength under unconstrained environments. The proposed algorithm reduces the error percentage even in the presence of types of noise include iris obstructions and specular reflection. The proposed algorithm starts with determining the expected region of the iris using the K-means clustering algorithm. The Circular Hough Transform (CHT) is then employed in order to estimate the iris radius and center. A new efficient algorithm is developed to detect and isolate the upper eyelids. Finally, the non-iris regions are removed. Results of applying the proposed algorithm on UBIRIS iris image databases demonstrate that it improves the segmentation accuracy and time. Iris segmentation plays an important role in an accurate iris recognition system. In less constrained environments where iris images are captured at-a-distance and on-the-move, iris segmentation becomes much more difficult due to the effects of significant variation of eye position and size, eyebrows, eyelashes, glasses and contact lenses, and hair, together with illumination changes and varying focus condition. This paper contributes to robust and accurate iris segmentation in very noisy images. Our main contributions are as follows: (1) we propose a limbic boundary localization algorithm that combines K-Means clustering based on the gray-level co-occurrence histogram and an improved Hough transform, and, in possible failures, a complementary method that uses skin information; the best localization between this and the former is selected. (2) An upper eyelid detection approach is presented, which combines a parabolic integro-differential operator and a RANSAC (RANdom SAmple Consensus)-like technique that utilizes edgels detected by a one-dimensional edge detector. (3) A segmentation approach is presented that exploits various techniques and different image information, following the idea of focus of attention, which progressively detects the eye, localizes the limbic and then pupillary boundaries, locates the eyelids and removes the specular highlight. The proposed method was evaluated in the UBIRIS.v2 testing database by the NICE.I organizing committee. We were ranked #4 among all participants according to the evaluation results.