Previous Works About Iris Recognition Stages A Brief Survey Sasan Harifi Faculty of Electrical, Computer and IT Engineering, Islamic Azad University, Qazvin Branch, Iran sasan.harifi@gmail.com Azam Bastanfard Faculty of Media Engineering, Islamic Republic of Iran Broadcast University, Tehran, Iran bastanfard@iribu.ac.ir Abstract—By increasing security requirements, biometric as a perfect solution for people identification is used. One of the biometrics which is considered as the most accurate and reliable method, is iris biometric. The iris biometric is about analysis of patterns in the iris texture. The operation can be done in several stages. Image acquisition, preprocessing and main processing including segmentation, normalization, feature extraction and matching are different stages of iris recognition. In this paper the previous works for different stages are proposed separately and classified, with the appropriate tables. Since there are many different methods for iris recognition, this survey covers some of the significant methods. Keywords— Iris recognition, Identification, Image acquisition, Iris challenges, Preprocessing. I. INTRODUCTION The human iris has unusually complex structure which has a lot of information in its context for iris biometric. Today, Due to the advancement of algorithms, iris recognition has developed. In general, iris recognition includes image acquisition stage, preprocessing stage and the main processing stage which the main processing stages are: segmentation, normalization, feature extraction and matching. Figure 1 shows a diagram of iris recognition stages. Fig. 1. Diagram of iris recognition stages This paper is about different methods of iris recognition stages. Rest of the paper is structured as follows: In section II image acquisition methods and available devices are mentioned. There is also information about existing databases. In the section III the preprocessing methods are presented. Section IV proposed methods in main processing stages of iris recognition, separately. Section V is brief evaluation, section VI described challenges in iris recognition and Section VII is conclusion. II. IMAGE ACQUISITION A. Approaches to Image Acquisition Researchers have studied about image acquisition, such as the wavelength of light, type of light source, the light reflected from the surface of the iris which is related to imaging sensor, the lens features, signal to noise ratio, eye safety and image quality. Current iris recognition systems operate with illumination in the 720–900nm range. This choice is driven by the absorption spectrum of melanin and the absorption spectrum of silicon. Melanin is the organic pigment that predominates in the coloring of human irises. It is strongly absorbing in the visible, but much less so in the near infrared (NIR) 800–900nm. People with dark eyes have more melanin than those with light eyes. Dark and light irises are more similar in their light reflecting and absorbing properties in the NIR than they are in the visible because the absorption of the melanin drops off in the NIR. Hence, the structural details of a dark iris show much better contrast in the NIR than in the visible [1]. There are still important issues in research on iris image acquisition. One of the issues is taking image of iris which is "at distance" or "at move". Sarnoff demonstrated its Iris on the Move TM portal system at the Biometrics Consortium Conference. The system was built for the US Government and was described in detail by Matey [1]. It was capable of acquiring iris images at distances of approximately 2–3m with the subject walking at a normal pace. Several copies of the system were constructed for the US Government. The long distance record for a system with co-located sensor and illumination was held, at least for a time, by another system that Sarnoff built for the US Government. This iris at a distance system is based on a Meade LX200-RF/10 8 inch reflecting telescope and a long distance illuminator consisting of an 850 nm LED focused on target to produce irradiance of approximately 1 mW/cm 2 . There has not been a publication describing the details of this device, Iris Recognition Main Processing Image Acquisition Preprocessing Iris Segmentation Normalization Feature Extraction Matching Recognition Results Proceedings of the Fourth International Conference on e-Technologies and Networks for Development, Lodz, Poland, 2015 ISBN: 978-1-4799-8450-3 ©2015 IEEE 6