Fast Iris Detection via Shape based Circularity Milos Stojmenovic, Aleksandar Jevremovic, Amiya Nayak Singidunum University, Belgrade, Serbia {mstojmenovic, ajevremovic}@singidunum.ac.rs SEECS, University of Ottawa, Canada nayak@uottawa.ca Abstract— We define an iris detection algorithm that performs an order of magnitude faster than the state of the art, while preserving accuracy. The algorithm isolates the pupil boundary by extracting image edges, then finding the largest contiguous set of points that satisfy the circularity criterion and contain mostly dark pixels. The iris/sclera boundary is found by horizontally and simultaneously searching along both directions of the pupil center for the highest cumulative difference in intensities. Current detection systems mainly rely on the methods proposed by [D] in order to isolate the iris pattern reliably, yet they are computationally expensive and expectedly slow. We apply a measure of circularity to isolate both the sclera and pupil boundaries, avoiding the exhaustive search required by [D]. Our method correctly identifies the iris region in 95% of test cases in the CASIA 3 dataset [CA]. While the detection rate is slightly lower compared to competitors, our method performs in O(n 2 ) time compared to Ω(n 3 ) that [D] offers. The iris detection procedure of [D], implemented in Matlab runs an average of roughly 15 seconds per image, while our own implementation in C++ on a single core 2.0 Ghz processor takes about 5 milliseconds on the same system, which is also faster than [HAK]. Keywords: Iris Detection, Circularity, Image Processing. I. INTRODUCTION Like other measures for primitive geometric shapes, the measure of circularity is motivated by real world image processing problems, in this case, biometrics. Circularity is common in nature and industry, and finding a way of identifying it can be important for potential applications to both. The main part of our solution involves measuring how circular a finite set of points is. In analyzing various algorithms, we restrict ourselves to the following criteria. Circularity values are assigned to sets of points and these values shall be numbers in the range [0, 1]. The circularity measure equals 1 if and only if the shape is a circle, and equals 0 when the shape is highly non-circular such as a line. A shape’s circularity value should be invariant under similarity transformations of the shape, such as scaling, rotation and translation. The algorithms should also be resistant to protrusions in the data set. Circularity values should also be computed by a simple and fast algorithm for any finite set of points. Using such a framework, we are able to quickly detect circular regions as candidates to pupil and sclera boundary detection. Circularity measures were discussed in [LS, DD, CKT, KA, P]. All of them are area based and linked to closed curves except for one: [P]. This one is shape based and can be applied to open curves. Daugman’s method [D] extracts the iris area by using an integrodifferential circular edge detector which tests a given point and radius (x, y, r) for circular characteristics. An exhaustive search is performed over the entire image for circular regions. Once the optimal locations of the circles have been determined, the area between them is used for iris comparison. [ZTW] detects the pupil by assuming it is the largest dark spot in the iris image, and the outer boundary of the iris by "maximizing changes of the perimeter-normalized sum of gray level values along the circle." [RB, FH] manually localized the iris, while [YZW] find the center of the pupil by first applying a binary threshold, and then finding the longest consecutive sequence of black pixels while searching through each horizontal line of the binary image. [HAK] introduces a real time solution to iris detection which is based on a combination of morphology, thresholding and an incremental, horizontal search for the pupil. We propose shape based algorithms that assign circularity values to both open and closed curves. These measures are adaptations of the linearity measures proposed in [SNZ]. The choice of center of each shape influences its overall circularity value. The center of each shape is traditionally seen as its center of gravity. We also consider another definition of shape center here. The ‘true center’ (X tc , Y tc ) of a shape is defined as the center of a circle C that best fits the shape to C. It is determined by sampling k triplets of points from the point set, and finding their true centers. The median center value of the k samples is taken as the shape’s true center. We use both cases of centers of shapes in order to eliminate instances where a boundary’s center is outside the image. We successfully apply our algorithm to the problem of iris detection in the CASIA V3 near infrared iris dataset which contains 2655 iris images from 249 subjects. Our algorithm was able to detect 95% of all irises (sclera and pupil boundaries), at an order of magnitude faster than [D]. The literature review is given in section 2. Our circularity measure and iris detection procedure are seen in section 3. Experimental data are presented in section 4, along with a general discussion of our results. II. LITERATURE REVIEW A. Iris Detection Daugman's iris recognition algorithm [D] first became commercialized in the 1990s. The algorithm automatically recognizes persons in real-time by encoding the random patterns visible in the iris of the eye from some distance, and applying a powerful test of statistical independence. It is currently used in many identification applications such as