International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064 Volume 2 Issue 9, September 2013 www.ijsr.net Comparison of Phase Only Correlation and Neural Network for Iris Recognition Bakliwal Suricha 1 , Mathur Garima 2 ,Yadav R.P 3 1 Jaipur Engineering College, Kukas, RTU, Dept. of Digital Communications, SP 43, RIICO Industrial Area, Delhi Road, Kukas, Jaipur-303101, India 2 Jaipur Engineering College, Kukas, RTU, Dept. of Electronics & Communications, SP 43, RIICO Industrial Area, Delhi Road, Kukas, Jaipur-303101, India 3 Malviya Regional Engineering College, Dept. of Electronics & Communications, MNIT, Jaipur-302017, India Abstract: This paper compares two different techniques of iris recognition and explains the steps of extracting iris from eye image palette formation and conditioning of palette for matching. The focus of the paper is in finding the suitable method for iris recognition on the basis of recognition time, recognition rate, false detection rate, conditioning time, algorithm complexity, bulk detection, database handling. In this paper comparison has been performed two methods feature based and phase based recognition. Keywords: Iris recognition, POC (Phase Only Correlation). 1. Introduction Iris recognition [2] using biometric authentication uses high- resolution images of the irises of individual’s eyes for pattern-recognition techniques. The camera technology used by Iris Recognition with restrained infrared illumination reducing specular reflection from the convex cornea, to create images of the detail-rich, complex structures of the iris. Conversion to digital templates, images mathematically represent iris that yield explicit positive identification of an individual [1]. Iris recognition [7] efficiency is rarely restricted on by glasses or contact lenses. It’s special characteristics of comparison speed, iris recognition becomes the only biometric technology compatible for one-to-many identification. Stability is the key advantage of iris recognition. The human iris is the annular part between the dark pupil and the white sclera; it is stable and distinctive throughout life. In an iris recognition system, iris localization is one of the most important steps, and has great influence on the subsequent feature extraction and classification. It aims to find the radii, centre and the parameters for both inner and outer boundaries of the iris. There are many iris localization methods; more algorithms were developed to improve the performance of iris localization. More recently, other algorithms have been presented by many researchers. However, these methods have promising performance; they need to search the iris boundaries over large parameter space thoroughly, which takes more computing time. Moreover, they may result in circle detection failure, because some selected threshold values used for edge detection cause critical edge points being removed. In this paper, we compare two algorithms for iris recognition from iris template images. The paper also explains the iris extraction from eye image. The paper is arranged in following way, section 2 explains the method of iris extraction by using Hough circle method, and then the conversion of circular iris image into rectangular template, section 2.3 explains the methods of iris recognition then after section 4 shows the simulated results and conclusion on the basis of results. Most of the researchers in the field of iris recognition use iris images from the following databases, which are available freely online:  The Chinese Academy of Sciences database (CASIA)[16]  The Bath database, produced by the university of Bath[17] 2. Pre Processing For Iris Recognition The realization of this work supposes the availability of a great number of repetitions of samples responding to the same known theoretical model. In practice, because of unknown theoretical model, the Monte-Carlo method is used based on the generation of the data by computer according to a fixed theoretical model. 2.1. Iris Boundaries Detection Iris is the area between pupil and white section of eyes, as shown in figures 2.1 the area between two white circles. Figure 2.1: Eye and iris Paper ID: 02013289 141