SHREE DEVI INSTITUTE OF TECHNOLOGY DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
System Requirement Specification (SRS)
IRIS RECOGNITION SECURITY
Submitted by: Ahammed Imthiyaz.O.M Ajith Jacob Ebin Babuji Hasif .M
Submitted to:
Deepika Attavar Lecturer in CSE dept.
INTRODUCTION The project “Iris Recognition Security” is a process of recognizing a person by analyzing the apparent pattern of his or her iris. Biometrics is an automated means of identifying an individual based on measurable biological or behavioral characteristics. The concept of using the biological characteristics of an individual for identification has been around for centuries. The simplest form of recognition began when men recognized faces as belonging to individuals of their own tribe. In comparison to face, fingerprint and other biometric traits there is still a great need for substantial mathematical and computer-vision research and insight into iris recognition. Iris recognition is the most powerful biometric technology. Nothing else comes close.
Most accurate
Scalable
Opt-in
Non-contact Interoperable cameras
The iris is the plainly visible, colored ring that surrounds the pupil. It is a muscular structure that controls the amount of light entering the eye, with intricate details that can be measured, such as striations, pits, and furrows. The iris is not to be confused with the retina, which lines the inside of the back of the eye. No two irises are alike. There is no detailed correlation between the iris patterns of even identical twins, or the right and left eye of an individual. The amount of information that can be measured in a single iris is much greater than fingerprints, and the accuracy is greater than DNA. The overall objective of this project is that the program converts a photo of an eye to an 'unrolled' depiction of the subject's iris and matches the eye to the agent's memory. If a match is found, it outputs a best match. The current functionality matches that proposed in the original requirements.
Problem Statements
Iris recognition systems are the most reliable biometric system since iris patterns are unique to each individual and do not change with times. In case of facial recognition some problems is there that it cannot handle pose variations. Another problem with facial recognition is sensitive to lighting variations and shadows,etc In case of fingerprint Vulnerable to noise and distortion brought on by dirt and twists. Some people may feel offended about placing their fingers on the same place where many other people have continuously touched. Some people have damaged or eliminated fingerprints. Fingerprints may be distorted and unreadable or unidentifiable if the person's fingertip has dirt on it, or if the finger is twisted during the process of fingerprinting
FEASIBLITY STUDY Literature Survey I r is Segmentati on M ethodologies
Much advancement have been made in the field of iris segmentation techniques. In 1993, J.G Daugman proposed an approach for iris segmentation. In the segmentation stage, this author introduced an integrodifferential operator to find both the iris inner and outer borders. The methodology used by him is most popular amongst all iris recognition techniques. In this paper, Daugman assumed the iris and pupil to be circular and introduced an operator for edge detection. This operator searches over the image domain (x, y) for the maximum in the blurred derivative with respect to increasing radius r, of the normalized contour integral of I(x, y) along a circular arc ds of radius r and center (X0 , Y0). A methodology was proposed by Wildes in 1997 in which the intensity values of the image is converted into a binary edge map. The edge map is constructed through the Canny edge detector. In order to incorporate directional tuning, the image intensity derivatives are weighted to favour ranges of orientation. Then the well-known Circular Hough Transform is used to obtain the boundaries. The accuracy of this methodology is dependent on the edge detection algorithm. In 2004, J. Huang proposed an approach which would work for iris images having noise. This method involved rough localization and normalization, edge information extraction and the fusion of edge and region information[11]. In a paper by P.Gupta et al(2006), Circular Hough Transform was used for detection of outer iris and inner iris boundaries. The procedure first finds the intensity image gradient at all the locations in the given image by convolving with the sobel filters. The absolute value of the gradient images along the vertical and horizontal direction is obtained to form an absolute gradient image. The absolute gradient image is used to find edges Motivation
The proposed methodologies as discussed in above section performed well with the good images. However, each methodology has one or more drawbacks. The main disadvantage with the mentioned techniques is they do not give good results with occluded images or images containing some noise due to eyelids, eyelashes, reflections etc. We were motivated to study and implement a technique which works with almost all kind of noisy images. The work is related to an approach to address the sub problem of iris localization which deals with localizing the iris and
pupil boundaries to produce data which can be used in the subsequent steps of iris recognition. Since iris is a robust and unique biometric data, researchers have developed many techniques to deploy its uniqueness to generate a biometric system which is reliable and accurate. However the iris images are not tolerant to noise the presence of which causes problems while localizing iris boundaries with the conventional techniques, we implement a technique of iris localization which gives better results with non-cooperative images. Existing System Fingerprint identification is the most well-known and publicized biometrics. Fingerprint Recognition refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify individuals and verify their identity. This article touches on two major classes of algorithms (minutia and pattern) and four sensor designs (optical, ultrasonic, passive capacitance, and active capacitance).
Another biometric security is the facial recognition which is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database.
SOFTWARE REQUIREMENT SPECIFICATIONS
A software requirement specification (SRS) is a complete description of the behavior of the system to be developed. SRS is a document that completely describes what the proposed software should do without describing how the software will do it. It is a two way policy that assures that both the client and the organization understand the requirements at any way given point of time. SRS document itself is a precise and it provides the functions and capabilities of a system that it should provide. It acts as a blueprint for completing a project such as it is cost effective. It provides feedback to customer. The basic purpose of SRS is to bridge the communication gap between the parties involved in the development of the software. SRS is the medium through which the client and user needs are accurately specified. An SRS is customer assurance that the development organization understands the issues or problems to be solved and the software behavior necessary to address those problems. It serves as an input to design specification. It also serves as the parent document to subsequent documents. Therefore, the SRS should be easy to understand and also should contain sufficient details in the system requirements so that design solution can be devised easily. a. Purpose Objective of this project is that the program converts a photo of an eye to an 'unrolled' depiction of the subject's iris and matches the eye to the agent's memory. If a match is found, it outputs a best match. b. Scope It can be used for security purposes and future immigration purposes.
c. Overall Description I.
II.
Product Perspective Introduction: Iris Recognition Input: Iris Image Output: Iris image is matching or not. Processing: Stored in database. Product Function
d. Specific Requirements
User interface: 1. Correctly identify the iris in the given photograph. 2. Correctly match the subject to a known identity. 3. Determine the subject's existence in the database within 10 seconds. 4. Must be able to properly identify a mismatched subject on a subsequent attempt. Software interface: 1. Java 2 SE Development Kit 6.0 2. Netbeans IDE 5.5 3. Microsoft SQL Server 2005 Hardware interface: 1. Pentium IV 1 GHZ clock speed 2. 256 MB of RAM
e. Functional Requirements Module 1: 1. 2. 3. 4.
Name of the module: Login Form Input: username and password Definition: Username and password is used to authorize application. Output: Application interface is accessible if username and password is correct , else interface cannot be usable for users
