International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 9, Issue 2 (November 2013), PP. 30-35 30 Iris feature extraction and recognition based on different transforms Mrs. Minakshi R. Rajput Asst. Professor PES College of engg, Aurangabad Abstract:- This paper reviewed the literature regarding iris recognition. It explains need and significance of this research .Hypothesis on iris recognition is also explored . Different stages of iris recognitions are also explained and at the last it clarifies how Contoulet Transform is more admissible for iris feature extraction. Keywords:- Biometrics,contourlet transform,feature extraction,iris recognition I. INTRODUCTION Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is one of the most reliable biometrics in terms of recognition and identification performance. Image feature extraction is one of the basic works for biometric analysis. In different methods of Biometrics, recognition by iris images in recent years has been taken into consideration by researchers as one of the common methods of identification like passwords, credit cards or keys. Iris recognition a new biometric technology has great advantages such as variability, stability and security. Although the area of the iris is small it has enormous pattern variability which makes it unique for every one as shown in figure 1. And hence leads to high reliability [1]. Fig 1: Distinctiveness of Human Iris In this paper ,different feature extraction method are explained for iris recognition which are based on different transforms .Also a new method based on contourlet transform is proposed. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales .So for reducing the feature vector dimensions we can use the method to extract only significant bit and information from normalized iris images. Contourlets not only possess the main features of wavelets (namely, multiscale and time-frequency localization), but also offer a high degree of directionality and anisotropy. For analyzing the desired performance of our proposed method, we use the CASIA dataset, which is comprised of 108 classes with 7 images in each class and each class represented a person. II. NEED AND SIGNIFICANCE OF THE RESEACH Research on Iris feature extraction using different tranforms is needed because of following reasons. 1. Iris recognition is the most robust and accurate biometric technologies available in the market today with existing large scale applications supporting databases in excess of millions of people. The Iris is a protected internal organ whose random texture is stable throughout life and can be used as an identity document or a password offering a very high degree of identity assurance. The randomness of Iris patterns has very high dimensionality; recognition decisions are made with very high confidence levels supporting rapid and reliable exhaustive searches through national - sized databases in both 1:1 (verification) and 1: n (identification) mode with no human intervention. 2. Real-time, high confidence recognition of a person's identity is needed now a days. mathematical analysis of the random patterns that are visible within the iris of an eye from some distance is possible and because of this Iris recognition can be used as a real time person identification system. The randomness of iris patterns has very high dimensionality; recognition decisions are made with confidence levels high enough to support rapid and reliable exhaustive searches through national-sized databases. 3. Iris recognition system Perform 1: n identification with no limitation on numbers.