An Approach of Iris Feature Extraction for Personal identification C.M.Patil, Sudarshan Patilkulkarani Research Scholar Assistant Professor JSS Research Foundation Dept of Electronics &Communication S J College of Engineering S J College of Engineering Campus, Mysore-570006 Campus, Mysore-570006 Abstract—Iris recognition is one of the most reliable biometric technologies. The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has become an important issue in the field of iris recognition. In this paper, a wavelet-based quality measure for iris images is proposed. The proposed method includes three modules: image preprocessing, feature extraction and recognition modules. The feature extraction module adopts the wavelet transform as the discriminating features. Similarity between two iris images is estimated using Euclidean distance measures. Features extracted using higher level wavelet decompositions are shown to yield better clustering and higher success rate in recognition. Keywords—Biometric identification, Wavelet transforms, Iris recognition, Feature representation. 1. Introduction Identification of humans through biometric technologies is becoming common. Different biometric technologies like finger, face, voice, iris recognition, etc. use different behavioral or psychological characteristics of humans for recognition. Early systems used to have password and ID cards for verification but it has two major problems of forgotten passwords and stolen ID cards. Biometrics provided solution to these problems. Among the all biometrics, iris recognition has achieved highest recognition accuracy. An iris is a colored area between dark pupil and bright sclera. Iris has unique characteristics like stability of iris patterns throughout life time, not surgically modifiable. Its probability of uniqueness among all humans has made it a reliable and efficient human recognition technique. It can be used in many applications like controlled access, airports, ATM, etc. It is particularly good for automatic recognition because of its complex pattern of many distinctive features [1] such as arching ligaments, furrows, ridges, crypts, rings, corona, freckles, and a zigzag collarets. Some of these patterns may be seen in Figure 1. Figure 1: A Typical Iris Structure Eyelashes Pupil Reflection Lower Eyelid 2. Related Work Plenty of works are done on Iris Recognition System, since last 3-4 years. Most of the cases, authors claimed the better performance of speed in capturing images and recognition over the existing systems available at that time. To gather the knowledge, we have considered the following selective works. Daugman is the inventor of the most successful commercial iris recognition system now and published his wonderful results in 1993 [2]. He proposed an integrodifferential operator for localizing iris regions along with removing the possible eyelid noises [3]. Wildes [4-5] processed iris segmentation through simple filtering and histogram operations. Eyelid edges were detected when edge detectors were processed with horizontal and then modeled as parabolas. No direction preference leaded to the pupil boundary. Boles and Boashah [6], Lim et al. [7], Noh et al. [8] and Tisse et al. [9] mainly focused on the iris image representation and feature matching, and did not introduce the information about noise removing. Kong and Zhang presented a noise detection model in [10]. As all other methods, noise regions were segmented from original iris images. 2009 International Conference on Advances in Recent Technologies in Communication and Computing 978-0-7695-3845-7/09 $25.00 © 2009 IEEE DOI 10.1109/ARTCom.2009.14 796 2009 International Conference on Advances in Recent Technologies in Communication and Computing 978-0-7695-3845-7/09 $26.00 © 2009 IEEE DOI 10.1109/ARTCom.2009.14 796 2009 International Conference on Advances in Recent Technologies in Communication and Computing 978-0-7695-3845-7/09 $26.00 © 2009 IEEE DOI 10.1109/ARTCom.2009.14 796