XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE Iris Segmentation using Hough Transform and Daugman’s integro-differential Operator Jaideep Singh CSE(hons. IOT) Chandigarh University Mohali,PB,INDIA. ait.17bcs4566@gmail.com Er.Rajat Tiwari ECE Chandigarh University Mohali,PB,INDIA. er.rajattiwari@gmail.com Abstract—this paper presents the iris segmentation using hough transform to detect the inner (region between iris and pupil) as well as the outer boundary (region between iris and scelra ) of the iris. Iris Segmentation is the main sub-process of iris recognition as wrongly segmented iris can result in wrong iris localization, thus a failure for the whole system.The whole proposed system in this paper is implemented using web technologies ,which makes it easy to incorporate this system in an online iris recognition system. Keywords—segmentation, iris, Hough transform, convolution. I. INTRODUCTION Biometrics refers to the identification of an individual using the traits that he/she processes. It mainly revolves around two main categories: physiological (fingerprint, iris, etc.) or behavioral (signature, voice, etc.). Physiological methods are mostly preferred because they doesn’t change over time and cannot be altered by human intentionally. Out of all physiological identification method ,iris is mostly adopted Fig 1. Labelled diagram of human eye [1] Iris is the circular structure made up of tissues, fibers and pigments [2]. The iris pattern develops early during the birth of an individual and doesn’t changes with age. The uniqueness and rigidity of the iris pattern is not the only reason for wide adoption of iris recognition as faithful biometric but it has some additions pros such as contactless recognition, high accuracy and inability to be copied. II. SEGMENTATION METHODS A. Daugman’s integro differential operator Daugman in 1993 proposed the first successful iris segmentation method which uses Integro Differential operator [3]. This method considers the iris to be a perfect circle. He applied the Differential operator to the image domain in search of iris’s outer boundary first then within the iris’s outer boundary in search of pupil .The fact that the difference of intensity between the iris and sclera is more than pupil and iris was utilized by this method. Given a pre-processed image, I(x,y), the following operator can be used to determine the iris’s boundary. Daugman’s IDO is mathematically expressed as below (4) The operator searches over image domain (x,y) for maximum in the blurred partial derivative with respect to increasing radius r of the normalized contour integral of I(x,y) along a circular arc ds of radius r and center coordinates (xo, yo). The symbol * denotes the convolution operation and Gσ(r) is a smoothing function such as a Gaussian of scale σ (standard deviation) . To normalize the circular integral with respect to its perimeter, it is divided by 2πr [5]. In short, this operator behaves as circular edge detector blurred at a scale set by σ, which searches iteratively over image space through the parameter set {xo, yo, r} .First search is for iris’s outer boundary with higher value of σ . Once the iris’s outer boundary is localized, the search process with finer value of σ, for the iris inner boundary is carried out only within the predetermined region. The computation time associated with an iris’s outer boundary search process can be reduced by providing a range of estimates for the parameter r, that are close to the actual boundary radius. B. Circular Hough Transform Hough transform is a standard image analysis tool for finding curves that can be defined in a parametrical form