Abstract:Biometrics deals with the recognition of humans based on their unique physical characteristics. It can be based on face identification, iris, fingerprint and DNA. In this paper, we have considered the iris as a source of biometric verification as it is the unique part of eye which can never be altered, and it remains the same throughout the life of an individual. We have proposed the improved iris recognition system including image registration as a main step as well as the edge detection method for feature extraction. The PCA-based method is also proposed as an independent iris recognition method based on a similarity score. Experiments conducted using our own developed database demonstrate that the first proposed system reduced the computation time to 6.56 sec, and it improved the accuracy to 99.73, while the PCA-based method has less accuracy than this system does.Keywords: biometrics; iris recognition; security system; image processing; pattern recognition; iris image acquisition; image registration; PCA
Iris Recognition Matlab Code Free Download
It is possible to achieve face recognition using MATLAB code. The built-in class and function in MATLAB can be used to detect the face, eyes, nose, and mouth. The object vision.CascadeObjectDetector System of the computer vision system toolbox recognizes objects based on the Viola-Jones face detection algorithm.
It is available for free download on two open source platforms, Github and Matlab File Exchange. Making it available as open source code will allow researchers to work together to study, use and improve the algorithm, and to freely modify and distribute it. It also will enable users to incorporate the technology into computer vision and pattern recognition applications and other image-processing applications.
IRIS RECOGNITION Iris recognition is the process of recognizing a person by analyzing the random pattern of the iris. Iris scan biometrics employs the unique characteristics and features of the human iris in order to verify the identity of an individual. The iris is the area of the eye where it is pigmented or colored circle, usually brown or blue.Iris recognition systems use small, high-quality cameras to capture a black and white high-resolution photograph of the iris. This process takes only one to two seconds and provides the details of the iris that are mapped, recorded and stored for future matching/verification. This technology is considered to be one of the safest, fastest, and most accurate, noninvasive biometric technologies.
SCIENCE BEHIND THE TECHNOLOGY The design and implementation of a system for automated iris recognition can be subdivided in to 3. 1. Image Acquisition 2. Iris Localization and 3. Pattern Matching.
IMAGE ACQUISITION a) The iris recognition process begins with image acquisition. Image acquisition is a process which deals with the capturing of a high quality image of the iris with the help of a digital camera. a) It is desirable to acquire images of the iris with sufficient resolution and sharpness to support recognition. b) It is important to have good contrast in the interior iris pattern without any distraction in the image. c) These images must be well framed. The widely used recognition system is the Daugman system which captures images with the iris diameter typically between 100 and 200 pixels from a distance of 15,46 cm using a 330 mm lens.
IRIS LOCALISATION Iris localization is a process that delimits the iris from the rest of the acquired image. After the camera situates the eye, it narrows in from the right and left of the iris to locate its outer edge. It simultaneously locates the inner edge of the iris. Conversion of an iris image into a numeric code that can be easily manipulated is essential to its use. This process was developed by John Daugman with the help of an algorithm developed by him.
PATTERN MATCHING The Iris Code derived from this process is compared with previously generated Iris Code. This process is called pattern matching. Using integer XOR logic, a long vector of each iris code can be XORed to generate a new integer. Each of whose bits represent mismatch between the vectors being compared. The total number of 1s represents the total number of mismatches between the two binary codes. For two identical Iris Codes, the hamming distance is Zero. For perfectly unmatched Iris Codes, the hamming distance is 1
A broad patent for iris recognition expired in 2005 which opened it up to a bigger market. Today, iris recognition technology is used as a form of biometric identification that can verify the uniqueness of an individual with exceptional accuracy.
In this paper we proposed an effective algorithm for iris recognition. Present method relies on DWT based features and feature matching classification. The experimental result is encouraging. In order to evaluate the performance of the proposed method, the database is used . This database has different characteristics like illumination change, bad focus, image noises etc. 2ff7e9595c
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