This document summarizes the history and development of biometric security and iris recognition. It discusses how biometric identification using characteristics like fingerprints, facial features, and iris patterns has evolved from early manual systems to modern automated systems using computer and image processing technologies. Key developments include the first systematic fingerprint collection in 1858, the establishment of fingerprint bureaus in 1896, the proposal of using iris patterns for identification in 1936, and the first automated iris recognition system released commercially in 1995. Iris recognition is now widely used for secure authentication due to the iris having unique random patterns that remain stable throughout one's life.
The document discusses iris recognition as a biometric identification method. It provides a brief history of iris recognition from its proposal in 1939 to its implementation in 1990 by Dr. John Daugman who created algorithms for it. The document outlines the iris recognition process including iris localization, normalization, feature extraction using Gabor filters, and matching using techniques like Euclidean distance. It discusses advantages like accuracy and stability of iris patterns, and disadvantages such as cost and inability to capture images from certain positions.
The document discusses iris biometrics and an iris recognition system. It provides details on iris anatomy, image acquisition, preprocessing, iris localization including pupil and iris detection, iris normalization, feature extraction using Haar wavelets, and matching. It evaluates the system on three databases achieving over 94% accuracy with low false acceptance and rejection rates. Further work is proposed on fusion, dual extraction approaches, indexing large databases, and using local descriptors.
This document discusses iris recognition as a biometric method for uniquely identifying individuals. It begins by explaining biometrics and the need for identification methods due to advances in technology and globalization. It then describes the anatomy of the human eye and details how the iris is unique among individuals and stable over one's lifetime, making it suitable for recognition. The document explains John Daugman's algorithms for iris encoding and matching iris codes to identify individuals. It discusses applications of iris recognition including border control, ATM access, and forensic identification. The document concludes that iris recognition is a highly accurate and secure biometric method due to the statistical rarity of matching irises between individuals.
The document summarizes iris recognition as a biometric identification method. It describes the anatomy of the human eye and details how the iris has unique patterns that can be used to identify individuals. The summary explains that iris recognition works by imaging the iris, locating its boundaries, normalizing variations, and matching its texture patterns to encoded templates in a database. With over 200 identifying features, the iris provides very high accuracy for identification applications such as border control, ATMs, and computer login authentication.
implementation of finger vein authentication techniqueViraj Rajopadhye
Viraj Rajopadhye presents on finger vein biometrics. Finger vein patterns provide a promising biometric for personal identification as it is secure and convenient, identifying individuals in 0.8 seconds. Finger vein detection works by capturing the pattern of blood vessels in the finger using near-infrared light, which is partially absorbed by hemoglobin in the veins. The extracted vein patterns are then stored as templates and used for matching against registered users, with errors possible in false acceptance or rejection of matches. Finger vein biometrics could be implemented for security applications like military zones, ATMs, and embedded in devices with improved security, low complexity and power usage.
The document discusses iris recognition as the best biometric identification system. It provides an overview of the iris recognition process which involves iris localization, normalization, feature encoding, and matching. Real-world applications of iris recognition include the Aadhaar ID project in India and border security in the UAE. While highly accurate, iris recognition has some disadvantages like accuracy variations depending on imaging conditions and potential for fake iris lenses. Overall, iris recognition is described as a fast and accurate biometric technology that will become more common with further development to address current limitations.
This document discusses iris recognition as a biometric security method. It provides an overview of how iris recognition works, including segmentation of the iris region, normalization, and feature extraction and matching. The accuracy of iris recognition is close to 82%, with an equal error rate of 18.3%. While iris recognition has advantages like the uniqueness and stability of iris patterns, concerns include the high cost of implementation and challenges with non-ideal iris images under different lighting conditions.
The document discusses iris recognition as a biometric identification method. It provides a brief history of iris recognition from its proposal in 1939 to its implementation in 1990 by Dr. John Daugman who created algorithms for it. The document outlines the iris recognition process including iris localization, normalization, feature extraction using Gabor filters, and matching using techniques like Euclidean distance. It discusses advantages like accuracy and stability of iris patterns, and disadvantages such as cost and inability to capture images from certain positions.
The document discusses iris biometrics and an iris recognition system. It provides details on iris anatomy, image acquisition, preprocessing, iris localization including pupil and iris detection, iris normalization, feature extraction using Haar wavelets, and matching. It evaluates the system on three databases achieving over 94% accuracy with low false acceptance and rejection rates. Further work is proposed on fusion, dual extraction approaches, indexing large databases, and using local descriptors.
This document discusses iris recognition as a biometric method for uniquely identifying individuals. It begins by explaining biometrics and the need for identification methods due to advances in technology and globalization. It then describes the anatomy of the human eye and details how the iris is unique among individuals and stable over one's lifetime, making it suitable for recognition. The document explains John Daugman's algorithms for iris encoding and matching iris codes to identify individuals. It discusses applications of iris recognition including border control, ATM access, and forensic identification. The document concludes that iris recognition is a highly accurate and secure biometric method due to the statistical rarity of matching irises between individuals.
The document summarizes iris recognition as a biometric identification method. It describes the anatomy of the human eye and details how the iris has unique patterns that can be used to identify individuals. The summary explains that iris recognition works by imaging the iris, locating its boundaries, normalizing variations, and matching its texture patterns to encoded templates in a database. With over 200 identifying features, the iris provides very high accuracy for identification applications such as border control, ATMs, and computer login authentication.
implementation of finger vein authentication techniqueViraj Rajopadhye
Viraj Rajopadhye presents on finger vein biometrics. Finger vein patterns provide a promising biometric for personal identification as it is secure and convenient, identifying individuals in 0.8 seconds. Finger vein detection works by capturing the pattern of blood vessels in the finger using near-infrared light, which is partially absorbed by hemoglobin in the veins. The extracted vein patterns are then stored as templates and used for matching against registered users, with errors possible in false acceptance or rejection of matches. Finger vein biometrics could be implemented for security applications like military zones, ATMs, and embedded in devices with improved security, low complexity and power usage.
The document discusses iris recognition as the best biometric identification system. It provides an overview of the iris recognition process which involves iris localization, normalization, feature encoding, and matching. Real-world applications of iris recognition include the Aadhaar ID project in India and border security in the UAE. While highly accurate, iris recognition has some disadvantages like accuracy variations depending on imaging conditions and potential for fake iris lenses. Overall, iris recognition is described as a fast and accurate biometric technology that will become more common with further development to address current limitations.
This document discusses iris recognition as a biometric security method. It provides an overview of how iris recognition works, including segmentation of the iris region, normalization, and feature extraction and matching. The accuracy of iris recognition is close to 82%, with an equal error rate of 18.3%. While iris recognition has advantages like the uniqueness and stability of iris patterns, concerns include the high cost of implementation and challenges with non-ideal iris images under different lighting conditions.
The document presents an embedded real-time finger-vein recognition system for mobile devices. The system uses finger vein patterns as a biometric for authentication through image acquisition of the finger veins, processing the images through segmentation, enhancement and feature extraction, and human-machine communication. It was found to have high security, low power consumption, small size, quick response time of 0.8 seconds, and high accuracy with a low equal error rate of 0.07%.
Fingerprint recognition is a biometric technique that uses fingerprint patterns to identify or verify individuals. It works by extracting minutiae points like ridge endings and bifurcations from scanned fingerprints and matching them against a database. The process involves fingerprint acquisition using optical or semiconductor sensors, minutiae extraction after preprocessing and thinning the image, and minutiae matching for verification or identification. Fingerprint recognition has applications in security systems and has advantages of high accuracy and small storage requirements, though it can be affected by dirty or wounded fingers.
iris recognition system as means of unique identification Being Topper
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
Biometrics uses physiological characteristics like fingerprints, iris patterns, and voice to identify individuals. The iris, located around the pupil, regulates the size of the pupil and has complex random patterns that are unique to each person. Iris recognition uses cameras to capture an iris image, overlay a grid to analyze patterns, and compare it to stored templates to identify a person. Iris scanning is highly accurate for identification and authentication purposes across applications like border control, computer login, and financial transactions due to the iris having unique patterns that remain stable throughout life.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of biometrics are improving rapidly, making many new applications possible, particularly for fingerprinting in phones. Improvements in cameras and other electronics are making optical, capacitive, and ultrasound sensors better. Improvements in microprocessors are making the matching algorithms operate faster and with higher accuracy. We expect biometrics to become widely used in the next few years beginning with smart phones and followed by automobiles, homes, and offices. Better biometrics in smart phones will promote security and mobile commerce.
The document discusses iris recognition as a biometric identification method that uses pattern recognition techniques to identify individuals based on the unique patterns in their irises. It provides an overview of the history and development of iris recognition, describes the components of an iris recognition system including image acquisition, segmentation, normalization, and feature encoding, and discusses applications of iris recognition including uses for border control, computer login authentication, and other security purposes.
Iris recognition is an automated method of bio metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Retinal scanning is a different, ocular-based bio metric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.
Study and development of Iris Segmentation and Normalization TechniqueSunil Kumar Chawla
The document is a thesis presentation on studying and developing iris segmentation and normalization techniques. It contains an introduction to biometrics and iris recognition. The document discusses literature on iris segmentation and normalization methods. It also covers topics like the anatomy and properties of the iris, existing iris recognition systems, and issues regarding biometrics. The goal is to develop an iris recognition system and evaluate its performance.
This document discusses vein recognition technology, which uses patterns of veins in the fingers or other parts of the body to identify individuals. It explains that vein recognition works by using infrared light to scan veins just under the skin and extract a template of the vein patterns, which can then be compared to stored templates to match identity. The document outlines some of the prominent companies involved in vein recognition technology, the different areas of the body that can be used, advantages like its non-invasive nature, and challenges like cost and lack of government interest. It also discusses potential applications for logical access control and centralized information management.
This document discusses biometric security and its advantages over traditional password and PIN-based security methods. Biometrics provide increased security through unique physiological traits that cannot be easily guessed, shared, or stolen like passwords. Biometrics also increase convenience by eliminating the need to remember multiple passwords. Additionally, biometrics improve accountability by verifying user identity and activities more accurately than traditional methods. The document explores various biometric factors and how biometric systems work to authenticate users securely.
in terms of Forensic Science, how iris recognition is done and what are the key factors that should be kept in mind. It can be its Advantages, Disadvantages, Approaches and very importantly the working process.
SMART ATTENDANCE SYSTEM USING FACE RECOGNITION (233.pptxBikashUpadhaya1
This document presents a smart attendance system using face recognition. The system aims to automate the attendance process using face detection and recognition instead of manual or traditional methods. It discusses capturing student faces with a camera, training a database with student images, detecting faces in new images and matching them to the database to mark attendance accurately and reduce issues like proxy attendance. It provides an overview of the methodology, system design including data flow and architecture diagrams, and demonstrates the system with some sample outputs.
This document presents information on iris scanner technology from a presentation by Shams. It discusses what the iris is, why iris recognition is used, the history and development of iris recognition, how iris recognition systems work, advantages like the iris being unique and stable over time, and disadvantages like the small target size and it being obscured. The conclusion is that iris scanning is highly accurate and fast but still needs some development to become more widely used technology.
This document provides an overview of biometric pattern recognition. It defines biometrics as measuring and analyzing biological traits to automatically identify or verify individuals. Biometric techniques are classified as either physical (e.g. fingerprints, face recognition) or behavioral (e.g. voice, typing rhythm). The document then describes several biometric systems like fingerprinting, face recognition, and iris identification. While biometrics provide security advantages over passwords, they also have limitations such as cost and potential privacy issues. Overall, biometrics are an emerging area that could replace the need for pins, passwords, and keys in the future through increasing convenience and security.
This document summarizes a student project on human activity recognition using smartphones. A group of 4 students submitted the project to partially fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The project involved developing a system to recognize human activities using the accelerometer and gyroscope sensors in smartphones. Various machine learning algorithms were tested and evaluated on experimental data collected from smartphone sensors. The goal of the project was to create an accurate and lightweight activity recognition system for smartphones, while also exploring active learning methods to reduce the amount of labeled training data needed.
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
This document discusses finger vein authentication technology. It begins with an introduction and overview of biometrics and finger vein authentication. It then describes the four components of finger vein detection and authentication: image acquisition, pre-processing, extraction, and matching. It highlights benefits of finger vein authentication such as accuracy, speed, security, compact size, and difficulty to forge. It concludes with examples of applications for finger vein authentication such as PC login, identity management, time/attendance tracking, cashless catering, banking, and access control for secure areas.
Biometrics refers to using unique human characteristics for identification. Biometric systems work by recording and comparing biometric traits like fingerprints, iris scans, voice patterns etc. These systems provide fast and accurate identification, making biometrics more secure than traditional security methods. Some key uses of biometric systems include border control, law enforcement, and workplace timekeeping and access control.
The document discusses palm vein biometric authentication technology. It provides an overview of biometric technologies including fingerprint, face, iris, and palm vein scans. Palm vein technology uses infrared light to scan the vein patterns in the palm, which are unique to each individual, for identity verification purposes. The technology provides highly accurate authentication with false acceptance and rejection rates of 0.00008% and 0.01%. It has applications in banking, computers, ID cards, hospitals, and industries due to its accuracy, speed, and difficulty to forge.
This document describes a parking monitoring control system project created by a group of electrical engineering students. The system uses RFID sensors and an IR sensor to detect vehicles and available parking spaces. An Arduino microcontroller processes the sensor signals. A 16x24 LED matrix displays the status of parking spaces. A servo motor and cellular shield allow remote monitoring via SMS. The system aims to help drivers locate available spaces and provide data on parking usage.
This document summarizes a thesis on developing an open-source iris recognition system to verify the uniqueness and performance of the human iris as a biometric identifier. The system segments iris images, normalizes variations, encodes iris patterns using log-Gabor filters, and matches templates using Hamming distance. Testing on two databases achieved perfect recognition on 75 images but false accept and reject rates of 0.005% and 0.238% on 624 images, showing iris recognition can be reliable and accurate.
The document presents an embedded real-time finger-vein recognition system for mobile devices. The system uses finger vein patterns as a biometric for authentication through image acquisition of the finger veins, processing the images through segmentation, enhancement and feature extraction, and human-machine communication. It was found to have high security, low power consumption, small size, quick response time of 0.8 seconds, and high accuracy with a low equal error rate of 0.07%.
Fingerprint recognition is a biometric technique that uses fingerprint patterns to identify or verify individuals. It works by extracting minutiae points like ridge endings and bifurcations from scanned fingerprints and matching them against a database. The process involves fingerprint acquisition using optical or semiconductor sensors, minutiae extraction after preprocessing and thinning the image, and minutiae matching for verification or identification. Fingerprint recognition has applications in security systems and has advantages of high accuracy and small storage requirements, though it can be affected by dirty or wounded fingers.
iris recognition system as means of unique identification Being Topper
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
Biometrics uses physiological characteristics like fingerprints, iris patterns, and voice to identify individuals. The iris, located around the pupil, regulates the size of the pupil and has complex random patterns that are unique to each person. Iris recognition uses cameras to capture an iris image, overlay a grid to analyze patterns, and compare it to stored templates to identify a person. Iris scanning is highly accurate for identification and authentication purposes across applications like border control, computer login, and financial transactions due to the iris having unique patterns that remain stable throughout life.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of biometrics are improving rapidly, making many new applications possible, particularly for fingerprinting in phones. Improvements in cameras and other electronics are making optical, capacitive, and ultrasound sensors better. Improvements in microprocessors are making the matching algorithms operate faster and with higher accuracy. We expect biometrics to become widely used in the next few years beginning with smart phones and followed by automobiles, homes, and offices. Better biometrics in smart phones will promote security and mobile commerce.
The document discusses iris recognition as a biometric identification method that uses pattern recognition techniques to identify individuals based on the unique patterns in their irises. It provides an overview of the history and development of iris recognition, describes the components of an iris recognition system including image acquisition, segmentation, normalization, and feature encoding, and discusses applications of iris recognition including uses for border control, computer login authentication, and other security purposes.
Iris recognition is an automated method of bio metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Retinal scanning is a different, ocular-based bio metric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.
Study and development of Iris Segmentation and Normalization TechniqueSunil Kumar Chawla
The document is a thesis presentation on studying and developing iris segmentation and normalization techniques. It contains an introduction to biometrics and iris recognition. The document discusses literature on iris segmentation and normalization methods. It also covers topics like the anatomy and properties of the iris, existing iris recognition systems, and issues regarding biometrics. The goal is to develop an iris recognition system and evaluate its performance.
This document discusses vein recognition technology, which uses patterns of veins in the fingers or other parts of the body to identify individuals. It explains that vein recognition works by using infrared light to scan veins just under the skin and extract a template of the vein patterns, which can then be compared to stored templates to match identity. The document outlines some of the prominent companies involved in vein recognition technology, the different areas of the body that can be used, advantages like its non-invasive nature, and challenges like cost and lack of government interest. It also discusses potential applications for logical access control and centralized information management.
This document discusses biometric security and its advantages over traditional password and PIN-based security methods. Biometrics provide increased security through unique physiological traits that cannot be easily guessed, shared, or stolen like passwords. Biometrics also increase convenience by eliminating the need to remember multiple passwords. Additionally, biometrics improve accountability by verifying user identity and activities more accurately than traditional methods. The document explores various biometric factors and how biometric systems work to authenticate users securely.
in terms of Forensic Science, how iris recognition is done and what are the key factors that should be kept in mind. It can be its Advantages, Disadvantages, Approaches and very importantly the working process.
SMART ATTENDANCE SYSTEM USING FACE RECOGNITION (233.pptxBikashUpadhaya1
This document presents a smart attendance system using face recognition. The system aims to automate the attendance process using face detection and recognition instead of manual or traditional methods. It discusses capturing student faces with a camera, training a database with student images, detecting faces in new images and matching them to the database to mark attendance accurately and reduce issues like proxy attendance. It provides an overview of the methodology, system design including data flow and architecture diagrams, and demonstrates the system with some sample outputs.
This document presents information on iris scanner technology from a presentation by Shams. It discusses what the iris is, why iris recognition is used, the history and development of iris recognition, how iris recognition systems work, advantages like the iris being unique and stable over time, and disadvantages like the small target size and it being obscured. The conclusion is that iris scanning is highly accurate and fast but still needs some development to become more widely used technology.
This document provides an overview of biometric pattern recognition. It defines biometrics as measuring and analyzing biological traits to automatically identify or verify individuals. Biometric techniques are classified as either physical (e.g. fingerprints, face recognition) or behavioral (e.g. voice, typing rhythm). The document then describes several biometric systems like fingerprinting, face recognition, and iris identification. While biometrics provide security advantages over passwords, they also have limitations such as cost and potential privacy issues. Overall, biometrics are an emerging area that could replace the need for pins, passwords, and keys in the future through increasing convenience and security.
This document summarizes a student project on human activity recognition using smartphones. A group of 4 students submitted the project to partially fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The project involved developing a system to recognize human activities using the accelerometer and gyroscope sensors in smartphones. Various machine learning algorithms were tested and evaluated on experimental data collected from smartphone sensors. The goal of the project was to create an accurate and lightweight activity recognition system for smartphones, while also exploring active learning methods to reduce the amount of labeled training data needed.
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
This document discusses finger vein authentication technology. It begins with an introduction and overview of biometrics and finger vein authentication. It then describes the four components of finger vein detection and authentication: image acquisition, pre-processing, extraction, and matching. It highlights benefits of finger vein authentication such as accuracy, speed, security, compact size, and difficulty to forge. It concludes with examples of applications for finger vein authentication such as PC login, identity management, time/attendance tracking, cashless catering, banking, and access control for secure areas.
Biometrics refers to using unique human characteristics for identification. Biometric systems work by recording and comparing biometric traits like fingerprints, iris scans, voice patterns etc. These systems provide fast and accurate identification, making biometrics more secure than traditional security methods. Some key uses of biometric systems include border control, law enforcement, and workplace timekeeping and access control.
The document discusses palm vein biometric authentication technology. It provides an overview of biometric technologies including fingerprint, face, iris, and palm vein scans. Palm vein technology uses infrared light to scan the vein patterns in the palm, which are unique to each individual, for identity verification purposes. The technology provides highly accurate authentication with false acceptance and rejection rates of 0.00008% and 0.01%. It has applications in banking, computers, ID cards, hospitals, and industries due to its accuracy, speed, and difficulty to forge.
This document describes a parking monitoring control system project created by a group of electrical engineering students. The system uses RFID sensors and an IR sensor to detect vehicles and available parking spaces. An Arduino microcontroller processes the sensor signals. A 16x24 LED matrix displays the status of parking spaces. A servo motor and cellular shield allow remote monitoring via SMS. The system aims to help drivers locate available spaces and provide data on parking usage.
This document summarizes a thesis on developing an open-source iris recognition system to verify the uniqueness and performance of the human iris as a biometric identifier. The system segments iris images, normalizes variations, encodes iris patterns using log-Gabor filters, and matches templates using Hamming distance. Testing on two databases achieved perfect recognition on 75 images but false accept and reject rates of 0.005% and 0.238% on 624 images, showing iris recognition can be reliable and accurate.
Design of a bionic hand using non invasive interfacemangal das
This document describes a project to design a bionic hand using a non-invasive interface. The project aims to restore motor function and limited sensory feedback to users who have lost a hand or arm. The design uses a control unit and sensors on the bionic hand. An input regulator circuit protects the user and microcontroller from voltage inputs. The microcontroller is programmed to receive input signals, decode them to control four motors in the hand, and receive feedback from pressure sensors. An amplification circuit powers the motors. The goal is to provide an easy-to-use bionic hand that improves users' ability to perform daily tasks.
The document is a project report submitted by three students - Rishabh Hastu, Parag Jagtap and Abhishek Shukla - for their Bachelor's degree. It examines security challenges in cognitive radio networks and proposes a two-stage solution. The first stage involves efficient spectrum sensing using eigenvalue-based energy detection. The second stage detects unauthorized malicious users using a security algorithm and encryption, which the malicious users cannot decrypt without the secret key. The project was carried out under the guidance of Prof. D.D. Ambawade at Bharatiya Vidya Bhavan’s Sardar Patel Institute of Technology, University of Mumbai.
The document is a special study report submitted by Shubham Madhukar Rokade to North Maharashtra University for their Bachelor of Engineering degree. It discusses wearable bio-sensors, including ring sensors and smart shirts. Ring sensors can continuously monitor heart rate and oxygen levels in an unobtrusive way using pulse oximetry. Smart shirts integrate sensors using optical fibers woven directly into the fabric to monitor vital signs without obstruction. The report provides details on the working, components, and applications of these wearable bio-sensing technologies.
This document discusses brain computer interfaces (BCIs), including their applications and challenges. It describes how BCIs can be used for medical purposes like rehabilitation as well as non-medical applications in education, marketing, entertainment and security. The document outlines different techniques for acquiring brain signals, such as invasive and noninvasive methods, and discusses the electrical signals measured from the brain. It also examines major challenges for BCIs, such as usability and technical issues, and potential solutions to address these challenges.
This document presents a system concept for instrumenting electric power utility towers with sensor technology. The concept involves distributing sensors on transmission structures and conductors to increase efficiency, reliability, safety and security of power transmission. Sensors may communicate wirelessly or via wired connections to data hubs installed on towers. Data is collected, stored and analyzed in a central database using wireless or wired communications between hubs and the database. The system aims to leverage advances in sensors, robotics, unmanned vehicles, satellites and wireless data transmission to enable automated inspection of transmission lines.
This document is a thesis submitted by Livinus Obiora Nweke for the degree of Master of Science in Computer Science. The thesis proposes a framework for validating network artifacts in digital forensics investigations based on stochastic and probabilistic modeling of the internal consistency of artifacts. The framework consists of three phases - data collection, feature selection using Monte Carlo Feature Selection, and a validation process using logistic regression analysis. The framework is demonstrated on network artifacts from intrusion detection systems. The experiment results show the validity of the network artifacts and can support assertions from the artifacts in investigations.
This document is a project report that proposes developing a web application to securely store files on a cloud server using hybrid cryptography. It aims to address data security and privacy issues for cloud storage. The application would use a hybrid cryptography technique combining symmetric and asymmetric encryption to encrypt files before uploading them to the cloud. Only authorized users with decryption keys would be able to access and download encrypted files from the cloud server. The report outlines the problem statement, objectives, methodology, design, and implementation of the proposed application to provide secure file storage on the cloud.
Smart Traffic Management System using Internet of Things (IoT)-btech-cse-04-0...TanuAgrawal27
This document presents a final year project report on developing a smart traffic management system using Internet of Things (IoT) technologies. It aims to optimize traffic light timing based on real-time vehicle counting data from road sensors. The proposed system would use sensors, microcontrollers, and cloud computing to monitor traffic flow and congestion at intersections, and dynamically adjust light durations on each lane accordingly. This is expected to reduce traffic delays and minimize commuting costs compared to traditional fixed-time traffic light systems. The report outlines the hardware, software, methodology, algorithms, and challenges of implementing such an IoT-based smart traffic management system.
This document is a project report on an Eye Tracking Interpretation System submitted by three students as a partial fulfillment of their Bachelor of Electronics and Telecommunication Engineering degree. It includes sections on introduction, literature survey, system description, software description, methodology, results, applications, and conclusion. The system uses an ultrasonic sensor and microcontroller to measure the distance to obstacles and displays it on an LCD screen. It aims to provide a low-cost solution for distance measurement that works in different light conditions including underwater.
Seminar Report on RFID Based Trackin SystemShahrikh Khan
The document is a seminar report submitted by Shahrukh Ayaz Khan on RFID based tracking system privacy control. It discusses RFID technology, how RFID works, applications of RFID, privacy and security issues related to RFID, and approaches to address these issues. The report contains an abstract, introduction discussing background and objectives of the report, literature review on related work and existing technologies, methodology covering RFID components and functioning, discussion on RFID security and privacy issues and solutions, analysis of advantages and disadvantages of RFID, and conclusion.
This thesis examines the wireless security of mobile applications, with a focus on banking apps, on the Android platform. The author conducted a static code analysis of apps on the Google Play Store and found widespread security flaws in how apps validate SSL certificates for secure connections. To address false positives from the static analysis, the author developed a method using dynamic code analysis and manual log file analysis to identify the critical code sections for certificate validation. The goal is to evaluate security and reduce false positives from the static analysis tool.
Vehicle to Vehicle Communication using Bluetooth and GPS.Mayur Wadekar
This document is a project report on vehicle to vehicle wireless communication using Bluetooth and GPS. It describes a system developed by four students to enable vehicles to share location data with each other using onboard GPS receivers and Bluetooth transmitters. The system aims to improve road safety by allowing vehicles to be aware of other nearby vehicles' positions. The report outlines the objectives, methodology, system components, implementation, performance analysis and applications of the proposed vehicle communication system.
iGUARD: An Intelligent Way To Secure - ReportNandu B Rajan
This document presents a project report for an intelligent door lock system called iGuard. It was submitted by Nandu B Rajan in partial fulfillment of the requirements for a Bachelor of Technology degree in computer science and engineering. The report includes sections on requirements analysis, system design, implementation, testing, and conclusions. It aims to develop a door lock system that provides strengthened security functions such as sending images of unauthorized access attempts to users and alerting users if the lock is physically damaged.
This document discusses ensuring the reliability of sensor systems. It begins by defining sensors and sensor systems, and noting the growing market for sensors being used in many industries. It then discusses how unreliable sensor systems can have serious consequences. Several factors that affect sensor reliability are identified, including the sensing element, operating environment, data processing, and aging. The document provides recommendations for improving reliability, such as choosing suitable sensor systems, implementing strict maintenance and calibration plans, and evaluating systems through testing and predictive modeling. The role of sensor data in digital twins and predictive maintenance is also examined. Overall, the document advocates for a holistic approach to ensure sensor system reliability.
This master's thesis describes the development of an interferometric biosensor. The biosensor uses an optical chip with a waveguide and double slits to create an interferometer based on Young's theory. Light is coupled into the chip and splits into sensing and reference beams that travel parallel paths. Changes in the sensing path due to biomolecular interactions are detected as phase changes in the interference pattern. The author aims to improve the optical system, characterize the chip coupling, develop measurement software, implement signal processing algorithms, and conduct test measurements to evaluate parameters like detection limit and drift. The interferometric biosensor allows label-free and real-time detection of biochemical reactions for applications in pharmaceutical and medical research.
With a massive influx of multi modality data,the role of data analytics in health
informatics has grown rapidly in the last decade. This has also prompted increasing
interests in the generation of analytical, data driven models based on machine learning in
health informatics. Deep learning, a technique with its foundation in artificial neural
networks, is emerging in recent years as a powerful tool for machine learning, promising
to reshape the future of artificial intelligence. Rapid improvements in computational
power, fast data storage, and parallelization have also contributed to the rapid uptake of
the technology in addition to its predictive power and ability to generate automatically
optimized high-level features and semantic interpretation from the input data. This article
presents a comprehensive up-todate review of research employing deep learning in health
informatics, providing a critical analysis of the relative merit, and potential pitfalls of the
technique as well as its future outlook. The paper mainly focuses on key applications of
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1. TRIBHUVAN UNIVERSITY
INSTITUTE OF ENGINEERING
HIMALAYA COLLEGE OF ENGINEERING
[CODE: CT 755]
A
FINAL YEAR PROJECT REPORT
ON
IRIS RECOGNITION SYSTEM
BY:
Bina Acharya (070/BCT/11)
Manjila Khanal (070/BCT/23)
Rabindra Khadka (070/BCT/35)
Radeep Chapagain (070/BCT/36)
A REPORT SUBMITTED TO DEPARTMENT OF ELECTRONICS AND
COMPUTER ENGINEERING IN PARTIAL FULFILLMENT OF THE
REQUIREMENT FOR BACHLORE’S DEGREE IN COMPUTER
ENGINEERING
DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINNERING
LALITPUR, NEPAL
AUGUST, 2017
2. ii
A
PROJECT REPORT
ON
IRIS RECOGNITION SYSTEM
Prepared For
Department of Electronics and Computer Engineering
Himalaya College of Engineering
Chyasal, Lalitpur
Prepared By
Bina Acharya (070/BCT/11)
Manjila Khanal (070/BCT/23)
Rabindra Khadka (070/BCT/35)
Radeep Chapagain (070/BCT/36)
AUGUST, 2017
3. iii
Acknowledgment
It gives us immense pleasure to express our deepest sense of gratitude and sincere
thanks to our highly respected supervisor Er. Hari Prasad Pokhrel, for his
insightful advice, motivating suggestions, invaluable guidance, support during the
process and constant encouragement and advice throughout our project hours.
We would like to express our sincere thanks to Er. Alok Kaflea (Project Co-
ordinator, Department of electronics and computer), for giving us the opportunity
to undertake this project. We express our deep gratitude to Er. Ashok GM (Head
of Department, Electronics and computer Engineering Himalaya College of
Engineering) for his regular support, co-operation, and co-ordination. The in-time
facilities provided by the department throughout the project hours are also equally
acknowledgeable.
We would like to convey our thanks to the teaching and non-teaching staffs of the
Department of Electronics and computer Engineering, HCOE for their invaluable
help and support throughout the period of the project hours. We will not miss to
express our gratitude to all our friends and everyone who has been the part of this
project by providing their comments and suggestions.
Bina Acharya
Manjila Khanal
Rabindra Khadka
Radeep Chapagai
4. iv
Abstract
This report on “Iris Recognition” is submitted in partial fulfillment of the
requirement for Computer Engineering.
“Iris Recognition” is a biometric application. A biometric system is a technological
system where a person is identified with the unique features posed by an individual.
Due to the increasing need of security this technique is gaining more popularity.
Several biometric features like finger, iris, voice, have been continuously
investigated and are still under consideration. Among this, iris recognition has been
a hot topic in pattern recognition and machine learning.
In this project we attempt to develop an app that identifies the person using the
unique pattern of his/her iris. In this app the person will be identified by matching
the features of their iris with the data stored in the database.
Keywords: Iris recognition, Biometric, Pattern recognition.
5. v
Table of Contents
Acknowledgment .....................................................................................................iii
Abstract ................................................................................................................... iv
Table of Contents.....................................................................................................v
List of Figures .........................................................................................................vii
List of Table........................................................... Error! Bookmark not defined.
Abbreviations.........................................................................................................viii
CHAPTER 1: INTRODUCTION ............................................................................1
1.1 Background ....................................................................................................2
1.2 Objective ........................................................................................................3
1.3 Problem Statement .........................................................................................3
1.4 Scope and Application ...................................................................................3
CHAPTER 2: LITERATURE REVIEW .................................................................4
2.1 Background ....................................................................................................5
2.2 Biometric Security..........................................................................................5
CHAPTER 3: REQUIREMENT ANALYSIS AND FEASIBLITY STUDY.........9
3.1 Feasibility Analysis......................................................................................10
3.1.1 Technical Feasibility .................................................................................. 10
3.1.2 Operational Feasibility............................................................................... 11
3.1.3 Economic Feasibility.................................................................................. 11
3.1.4 Schedule Feasibility................................................................................... 12
3.2 Requirement Definition................................................................................12
3.2.1 Functional Requirements........................................................................... 12
3.2.2 Non-Functional Requirements ................................................................... 12
3.3 Model and Software Process........................................................................13
CHAPTER 4: SYSTEM DESIGN AND ARCHITECTURE................................14
4.1 Use Case Diagram........................................................................................15
CHAPTER 5: METHODOLOGY .........................................................................17
6. vi
5.1 Image Acquisition........................................................................................18
5.2 Pre-Processing..............................................................................................18
5.2.1 Grayscale.................................................................................................. 18
5.2.2 Median filter............................................................................................. 19
5.2.3 Mean Filter............................................................................................... 20
5.3 Segmentation................................................................................................21
5.3.1 Pupil center detection............................................................................... 21
5.3.2 Canny edge detector................................................................................. 22
5.3.3 Iris radius detection................................................................................... 23
5.4 Normalization...............................................................................................23
5.5 Matching.......................................................................................................24
CHAPTER 6: TESTING........................................................................................25
6.1. Unit Testing.................................................................................................26
6.2. System Testing............................................................................................26
6.3. Performance Testing ...................................................................................27
6.4. Verification and Validation.........................................................................27
CHAPTER 7: DISCUSSION.............................................................................28
CHAPTER 8: CONCLUSION ..............................................................................29
Screenshots.............................................................................................................33
Reference ...............................................................................................................36
7. vii
List of Figures
Figure 1.1 Human Eye .............................................................................................2
Figure 5.2 System Architecture .............................................................................13
Figure 6.1 Original Gantt chart..............................................................................15
Figure 6.2 Current Gantt chart ...............................................................................16
8. viii
Abbreviations
App Application
ATM Automated Teller Machine
CASIA Chinese Academy of Science Institute
of Automation
Dr. Doctor
IEEE Institute of Electrical and Electronics
Engineering
Open CV Open Source Computer Vision
PINs Personal Identification Numbers
UI User Interface
10. 2
1.1 Background
A biometric system is a technological system where a person is identified with the
unique features possessed by an individual (like voice, fingerprint, facial features,
hand gestures, iris). In any biometric system first the sample of the feature is
captured which is transformed into a biometric template. This template is later on
compared with other templates to determine the identity.
The iris is a thin circular diaphragm, which lies between the cornea and the lens of
a human eye. The iris is close to the center by a circular aperture called pupil. The
average diameter of the iris is 12 mm and the size of pupil varies 10% to 80% of
the iris diameter. The unique pattern of the iris is random and not related to any
genetic factors formed during first year of life. Due to the epigenetic nature of iris
patterns identical twins possess uncorrelated iris patterns.
Figure 1.1 Human Eye
Compared to other biometric technologies, such as face, voice and fingerprint, iris
recognition can be considered as the most reliable form of biometric technology. In
addition iris has many special optical and physiological characteristics which can
be exploited to defend against possible forgery.[1]
11. 3
1.2 Objective
The main objective of our application is to identify an individual with high
efficiency and accuracy by analyzing the random patterns visible within the iris of
an eye.
1.3 Problem Statement
Conventionally passwords, secret codes and PINs are used for identification which
can be easily stolen, observed or forgotten. In pattern recognition problems, the key
issue is the relation between inter-class and intra-class variability: objects can be
reliably classified only if the variability among different instances of a given class
is less than the variability between different classes. For example in face
recognition, difficulties arise from the fact that the face is a changeable social organ
displaying a variety of expressions, as well as being an active 3D object whose
image varies with viewing angle, pose, illumination, accoutrements, and age. So as
an alternative we propose to use biometrics (iris recognition) system to identify an
individual.
1.4 Scope and Application
The purposed system is the iris recognition system that can be used in various fields
for identification and authentication process. Some of its applications are
Computer login: as a password
Secure access to bank accounts at ATM
Premises access control (home, office, laboratory, etc)
Forensics: birth certificates ; tracing missing or wanted persons
Credit card authentication
Secure financial transactions (e-commerce)
Anti – terrorism (e.g. security screening at airplanes)
Any existing use of keys, cards, PINs or password
13. 5
2.1 Background
Research on biometric methods has gained renewed attention in recent years
brought on by an increase in security concerns. The increasing crime rate has
influenced people and their governments to take action and be more proactive in
security issues. This need for security also extends to the need for individuals to
protect, among other things, their working environments, homes, personal
possessions and assets. Many biometric techniques have been developed and are
being improved with the most successful being applied in everyday law
enforcement and security applications. Biometric methods include several state-of-
the-art techniques. Among them, iris recognition is considered to be the most
powerful technique for security authentication in present context.
Advances in sensor technology and an increasing demand for biometrics are driving
a burgeoning biometric industry to develop new technologies. As commercial
incentives increase, many new technologies for person identification are being
developed, each with its own strengths and weaknesses and a potential niche
market.
2.2 Biometric Security
The term “Biometrics” is derived from the Greek words “bio” (life) and
“metrics” (to measure) (Rood and Hornak, 2008). Automated biometric
systems have only become available over the last few decades, due to the
significant advances in the field of computer and image processing.
Although biometric technology seems to belong in the twenty first century,
the history of biometrics goes back thousands of years. The ancient
Egyptians and the Chinese played a large role in biometrics history. Today,
the focus is on using biometric face recognition, iris recognition, retina
recognition and identifying characteristics to stop terrorism and improve
security measures. This section provides a brief history on biometric
security and fingerprint recognition.
During 1858, the first recorded systematic capture of hand and finger images
for identification purposes was used by Sir William Herschel, Civil Service
14. 6
of India, who recorded a handprint on the back of a contract for each worker
to distinguish employees (Komarinski, 2004).
During 1870, Alphonse Bertillon developed a method of identifying
individuals based on detailed records of their body measurements, physical
descriptions and photographs. This method was termed as “Bertillonage” or
anthropometrics and the usage was aborted in 1903 when it was discovered
that some people share same measurements and physical characteristics
(State University of New York at Canton, 2003).
Sir Francis Galton, in 1892, developed a classification system for
fingerprints using minutiae characteristics that is being used by researchers
and educationalists even today. Sir Edward Henry, during 1896, paved way
to the success of fingerprint recognition by using Galton's theory to identify
prisoners by their fingerprint impressions. He devised a classification
system that allowed thousands of fingerprints to be easily filed, searched
and traced. He helped in the first establishment of fingerprint bureau in the
same year and his method gained worldwide acceptance for identifying
criminals (Scottish Criminal Record Office, 2002).
The concept of using iris pattern for identification was first proposed by
Ophthalmologist Frank Burch in 1936 (Iradian Technologies, 2003). During
1960, the first semi-automatic face recognition system was developed by
Woodrow W. Bledsoe, which used the location of eyes, ears, nose and
mouth on the photographs for recognition purposes. In the same year, the
first model of acoustic speech production was creased by a Swedish
Professor, Gunnar Fant. His invention is used in today's speaker recognition
system (Woodward et al, 2003).
Sir Francis Galton, in 1892, developed a classification system for
fingerprints using minutiae characteristics that is being used by researchers
and educationalists even today. Sir Edward Henry, during 1896, paved way
to the success of fingerprint recognition by using Galton's theory to identify
prisoners by their fingerprint impressions. He devised a classification
system that allowed thousands of fingerprints to be easily filed, searched
and traced. He helped in the first establishment of fingerprint bureau in the
15. 7
same year and his method gained worldwide acceptance for identifying
criminals (Scottish Criminal Record Office, 2002).
The concept of using iris pattern for identification was first proposed by
Ophthalmologist Frank Burch in 1936 (Iradian Technologies, 2003). During
1960, the first semi-automatic face recognition system was developed by
Woodrow W. Bledsoe, which used the location of eyes, ears, nose and
mouth on the photographs for recognition purposes. In the same year, the
first model of acoustic speech production was creased by a Swedish
Professor, Gunnar Fant. His invention is used in today's speaker recognition
system (Woodward et al, 2003).
The first automated signature recognition system was developed by North
American Aviation during 1965 (Mauceri, 1965). This technique was later,
in 1969, used by Federal Bureau of Investigation (FBI) in their
investigations to reduce man hours invested in the analysis of signatures.
The year 1970 introduced face recognition towards authentication.
Goldstein et al. (1971) used 21 specific markers such as hair color, lip
thickness to automate the recognition process. The main disadvantage of
such a system was that all these features were manually identified and
computed.
During the same period, Dr.Joseph Perkell produced the first behavioral
components of speech to identify a person (Woodward et al, 2003). The first
commercial hand geometry system was made available in 1974 for physical
access control, time and attendance and personal identification. The success
of this first biometric automated system motivated several funding agencies
like FBI Fund, NIST for the development of scanners and feature extraction
technology (Ratha and Bolle, 2004), which will finally lead to the
development of a perfect human recognizer. This resulted in the first
prototype of speaker recognition system in 1976, which was developed by
Texas instruments and was tested by US Air Force and the MITRE
Corporation. In 1996, the hand geometry was implemented successfully at
the Olympic Games and the system implemented was able to handle the
enrollment of over 65,000 people.
16. 8
Drs. Leonard Flom and Aran Safir, in 1985, found out that no two irises are
alike and their findings were awarded a patent during 1986. In the year 1988,
the first semi-automated facial recognition system was deployed by
Lakewood Division of Los Angeles County Sheriff's Department for
identifying suspects (Angela, 2009). This was followed by several land
marked contributiona by Sirovich and Kirby (1989), Turk and Pentland
(1991), Philipis et al. (2000) in the field of face recognition.
The next stage in fingerprint automation occurred at the end of 1994 with
the Integrated Automated Fingerprint Identification System (IAFIS) 36
competition. The competition identified and investigated three major
challenges:
(1) Digital fingerprint acquisition
(2) Local ridge characteristic extraction and
(3) Ridge characteristic pattern matching (David et al., 2005).
The first Automated Fingerprint Identification System (AFIS) was
developed by Palm System in 1993. During 1995, the iris biometric was
officially released as a commercial authentication tool by Defense Nuclear
Agency and Iriscan.
The year 2000 envisaged the first face recognition vendor test (FRVT, 2000)
sponsored by the US Government agencies and the same year paved way
for the first research paper on the use of vascular patterns for recognition
(Im et al., 2001). During 2003, ICAO (International civil Aviation
Organization) adopted blueprints for the integration of biometric
identification information into passports and other Machine Readable
Travel Documents (MRTDs). Facial recognition was selected as the
globally interoperable biometric for machine-assisted identity confirmation
with MRTDs.
The first statewide automated palm print database was deployed by the US
in 2004. The Face Recognition Grand Challenge (FRGC) began in the same
year to improve the identification problem. In 2005, Iris on the move was
announced by Biometric Consortium Conference for enabling the collection
of iris images from individuals walking through a portal. [2]
18. 10
3.1 Feasibility Analysis
A feasibility study is a preliminary study which investigates the information of
prospective users and determines the resources requirements, costs, benefits and
feasibility of proposed system. A feasibility study takes into account various
constraints within which the system should be implemented and operated. In this
stage, the resource needed for the implementation such as computing equipment,
manpower and costs are estimated. The estimated are compared with available
resources and a cost benefit analysis of the system is made. The feasibility analysis
activity involves the analysis of the problem and collection of all relevant
information relating to the project. The main objectives of the feasibility study are
to determine whether the project would be feasible in terms of economic feasibility,
technical feasibility and operational feasibility and schedule feasibility or not. It is
to make sure that the input data which are required for the project are available.
Thus we evaluated the feasibility of the system in terms of the following categories:
Technical feasibility
Operational feasibility
Economic feasibility
Schedule feasibility
3.1.1 Technical Feasibility
Evaluating the technical feasibility is the trickiest part of a feasibility study. This is
because, at the point in time there is no any detailed designed of the system, making
it difficult to access issues like performance, costs (on account of the kind of
technology to be deployed) etc. A number of issues have to be considered while
doing a technical analysis; understand the different technologies involved in the
proposed system. Before commencing the project, we have to be very clear about
what are the technologies that are to be required for the development of the new
system. Is the required technology available? Iris recognition system is technically
feasible. All the tools necessary for this system is easily available. It uses NetBeans
19. 11
for application development. Though all the tools seem to be easily available, there
will be other challenges too.
3.1.2 Operational Feasibility
Proposed project is beneficial only if it can be turned into information systems that
will meet the operating requirements. Simply stated, this test of feasibility asks if
the system will work when it is developed and installed. Are there major barriers to
Implementation?
Since the proposed system was to help reduce the hardships encountered in the
current verification system, the new system was considered to be operational
feasible. The purpose of any project is that targeted audience/ client uses our
software. Thus it is necessary for developers to understand the need of targeted
audience and implement it in our software.
The targeted users of our system are any organization where authentication of
individual plays a vital role. Though it may not be 100% efficient but it makes easy
and minor error can be handled after the extraction.
3.1.3 Economic Feasibility
Economic feasibility attempts to weigh the costs of developing and implementing a
new system, against the benefits that would accrue from having the new system in
place. This feasibility study gives the top management the economic justification
for the new system. A simple economic analysis which gives the actual comparison
of costs and benefits are much more meaningful in this case. In addition, this proves
to be useful point of reference to compare actual costs as the project progresses.
There could be various types of intangible benefits on account of automation. These
could increase customer satisfaction, improvement in product quality, better
decision making, and timeliness of information, expediting activities, improved
accuracy of operations, better documentation and record keeping, faster retrieval of
information, better employee morale.
This application is an application based project. So tools for students can be
obtained at affordable price. The creation of the application is not costly.
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3.1.4 Schedule Feasibility
A project will fail if it takes too long to be completed before it is useful. Typically,
this means estimating how long the system will take to develop, and if it can be
completed in a given period of time using some methods like payback period.
Schedule feasibility is a measure how reasonable the project timetable is. Given our
technical expertise, are the project deadlines reasonable? Some project is initiated
with specific deadlines. It is necessary to determine whether the deadlines are
mandatory or desirable.
A minor deviation can be encountered in the original schedule decided at the
beginning of the project. The application development is feasible in terms of
schedule. [3]
3.2 Requirement Definition
After the extensive analysis of the problems in the system, we are familiarized with
the requirement that the current system needs. The requirement that the system
needs is categorized into the functional and non-functional requirements. These
requirements are listed below:
3.2.1 Functional Requirements
Functional requirement are the functions or features that must be included in any
system to satisfy the business needs and be acceptable to the users. Based on this,
the functional requirements that the system must require are as follows:
System should detect the individual on the basis of iris.
System should process the input given by the user only if it is an image
file.
3.2.2 Non-Functional Requirements
Non-functional requirements is a description of features, characteristics and
attribute of the system as well as any constraints that may limit the boundaries of
the proposed system. The non-functional requirements are essentially based on the
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performance, information, economy, control and security efficiency and services.
[4] Based on these the non-functional requirements are as follows:
User friendly
System should provide better accuracy
To perform with efficient throughput and response time
3.3 Model and Software Process
For the development of software starting from beginning to the completion of final
product, specific models have to be used. In software development life cycle, it is
possible to use any software models like waterfall, incremental, prototype agile etc.
based on the project requirements. In this project, agile model has been used. It is
also a type of incremental model in which software is developed in incremental and
rapid cycles. Whenever new changes have to be made, agile model allows easy
implementation at very little cost. It minimizes risk by developing software in small
iterations. Planning, developing and testing phase has been iteratively used to
implement new changes easily that reduces the risk of project failure.
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The project we are working on “Iris Recognition System” has been completed. We
have used Java as our platform. The whole system is further divided into
subsystems.
5.1 Image Acquisition
In this phase we acquire basic information from user like name, phone no, email id
and image of an eye. This information will be stored in the memory. For the image
first it goes through several process and then only gets stored in the memory.
5.2 Pre-Processing
Pre-processing of image refers to operations performed on image at the lowest level
of abstraction. Raw image without pre-processing may have a variety of problems,
and therefore it is not likely to produce the best computer vision results. The aim of
pre-processing is an improvement of image data that suppress unwanted distortions
or enhances some image features for further processing. Image pre-processing can
have a dramatic positive effects on the quality of feature extraction and the results
of image analysis. [5]
5.2.1 Grayscale Image
Grayscale is a range of shades of gray without apparent color. The darkest possible
is black and the lightest possible is white. Grayscale images use only one channel
of color. Converting an image to grayscale is a common technique in image
processing as a pre-processing technique. This is because of the benefits like the
processing operation should only be applied in a single plane with grayscale image,
it is simpler than using RGBA image. There are different methods for converting
an image into grayscale.
The lightness method averages the most prominent and least prominent colors:
(max(R, G, B) + min(R, G, B)) / 2.
The average method simply averages the values: (R + G + B) / 3.
The luminosity method is a more sophisticated version of the average method. It
also averages the values, but it forms a weighted average to account for human
perception. The formula for luminosity is 0.21 R + 0.72 G + 0.07 B. [6]
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We have used the luminosity method. Red, green and blue are not equally bright so
we are using a weighted average.
5.2.2 Median filter
The median filter is a non-linear digital filtering technique often used to remove
noise from an image. It preserves the useful information along with reducing noise.
Median filter considers each pixel in the image in turn and looks at its nearby
neighborhood. First all the pixel values from the surrounding are sorted and then
the pixel being considered is replaced by the middle pixel value. If the
neighborhood under consideration contains an even number of pixels, the average
of the two middle pixel values is used. An example calculation is shown below:
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[7]
5.2.3 Mean Filter
The mean filter is a simple sliding-window spatial filter that replaces the center
value in the window with the average (mean) of all the pixel values in the window.
Mean filtering is usually thought of as a convolution filter. Like other convolutions
it is based around a kernel, which represents the shape and size of the neighborhood
to be sampled when calculating the mean. Often a 3×3 square kernel is used,
although larger kernels (e.g. 5×5 squares) can be used for more severe smoothing.
The effect of applying small kernel more than once is similar but not identical as a
single pass with a large kernel.
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[8]
5.3 Segmentation
5.3.1 Pupil center detection
The main objective here is to detect the pupil center. The algorithm scans through
the median image from top left to bottom right. The algorithm makes no assumption
about position of pupil.
First it finds the pixel that is below threshold (a combination of lowest intensity in
current image and current variance). Then the amount of pixel (block size) adjacent
to its right that have an intensity below threshold are detected. Center of the detected
block is center of the pupil. But, if the block is largest observed (i.e. larger that
maximum block size ) it determines if the block of pixels going in vertical direction
up and down from center are also below threshold and some variance. If so, the max
block size is updated and the center of that block is new pupil center.
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5.3.2 Canny edge detector
Canny edge detector is an edge detection operator that uses a multi-stage algorithm
to detect a wide range of edges in an image. It detects the edges of the image based
on the current threshold and sigma values. This will generate the binary image that
shows the edges of the image. Canny edge detector aims to satisfy main three
criteria:
Low error rate: A good detection of only existing edges.
Good localization: Distance between edge pixels detected and real edge
pixels have to be minimized.
Minimal response: Only one detector response per edge.
The algorithm works in following steps:
1) Filter out any noise. The Gaussian filter is used for this purpose. An example
of Gaussian kernel of size=5 might be used as shown below:
2) Find the intensity gradient of the image. For this, we follow a procedure
analogous to sobel:
a) Apply a pair of convolution masks (in x and y directions):
b) Find the gradient strength and direction with:
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The direction is rounded to one of four possible angles (namely 0,
45, 90 or 135).
3) Non-maximum suppression is applied. This removes pixels that are not
considered to be part of an edge. Hence, only thin lines will remain.
4) Hysteresis is the final step. Canny does use two threshold (upper and
lower) :
a) If a pixel gradient is higher than the upper threshold, the pixel is
accepted as an edge.
b) If a pixel gradient is below the lower threshold, then it is rejected.
c) If the pixel gradient is between the two thresholds, then it will be
accepted only if it is connected to a pixel that is above the upper
threshold. [9]
5.3.3 Iris radius detection
Now that the pupil center and edge been detected we start with radius of pupil
identified by the pupil center detection and finds a radius for which the circle in the
edge image has at least a certain amount of black pixels (edges) on or nearly on the
circle. If the proportion of the iris radius meeting this criteria (radius of iris radius
to pupil radius should be of about 0.30 to 0.40) is between two bounds and the
percentage of pixels along the circle defined by the current radius and the pupil
center that are black is greater than certain percentage then the iris radius is
successfully detected.
5.4 Normalization
The main goal is to define the area between the pupil radius and the iris radius. For
this the iris a circular portion is transformed into rectangular. For each coordinate
in the image, we determine the polar angle and the distance between the radius of
the iris and pupil. We also determine the relative distance from the pupil radius to
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the point. Using this information we convert each polar coordinates to Cartesian co-
ordinates in each iteration. For this we use the formula:
X= cos (Ɵ)* r + (x-coordinate of center)
Y= sin (Ɵ)* r + (y-coordinate of center)
Where,
X= x Cartesian coordinate
Y= y Cartesian coordinate
r = radius of pupil and relative distance
Ɵ = angle of the current polar coordinate
Centre = pupil center
5.5 Matching
Initially the image is stored in database only after the unwrapping of the iris portion.
So, when a new identity is to be matched first the median filter is applied and then
pupil and iris center is detected and both radius is found. And then the region of the
iris is unrolled for the identity. Median filter is applied and then this image is
compared to the one in the database by subtracting the intensity of two images and
change in pixel is determined for each pixel. Then the average percentage change
per pixel between the subject and identity is determined. Iteratively, the percentage
change (probability) between the two images is compared for each image in the
database to find the best match. And finally the best match is identified.
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6.1. Unit Testing
Unit testing is performed for testing modules against detailed design. Inputs to the
process are usually compiled modules from the coding process. Each modules are
assembled into a larger unit during the unit testing process.
Testing has been performed on each phase of project design and coding. We carry
out the testing of module interface to ensure the proper flow of information into and
out of the program unit while testing. We make sure that the temporarily stored data
maintains its integrity throughout the algorithm's execution by examining the local
data structure. Finally, all error-handling paths are also tested. [10]
6.2. System Testing
We usually perform system testing to find errors resulting from unanticipated
interaction between the sub-system and system components. Software must be
tested to detect and rectify all possible errors once the source code is generated
before delivering it to the customers. For finding errors, series of test cases must be
developed which ultimately uncover all the possibly existing errors. Different
software techniques can be used for this process. These techniques provide
systematic guidance for designing test that
Exercise the internal logic of the software components,
Exercise the input and output domains of a program to uncover errors in
program function, behavior and performance.
We test the software using two methods:
White Box testing: Internal program logic is exercised using this test case design
techniques.
Black Box testing: Software requirements are exercised using this test case design
techniques.
Both techniques help in finding maximum number of errors with minimal effort and
time.
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6.3. Performance Testing
It is done to test the run-time performance of the software within the context of
integrated system. These tests are carried out throughout the testing process. For
example, the performance of individual module are accessed during white box
testing under unit testing.
6.4. Verification and Validation
The testing process is a part of broader subject referring to verification and
validation. We have to acknowledge the system specifications and try to meet the
customer’s requirements and for this sole purpose, we have to verify and validate
the product to make sure everything is in place. Verification and validation are two
different things. One is performed to ensure that the software correctly implements
a specific functionality and other is done to ensure if the customer requirements are
properly met or not by the end product.
Verification is more like 'are we building the product right?' and validation is more
like 'are we building the right product?’[11]
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7.1 Result analysis
After facing a number of errors, successful elimination of those error we have
completed our project with continuous effort. At the end of the project the results
can be summarized as:
A user friendly desktop application to use.
No expertise is required for using the application.
Organizations can use the application to authenticate individuals.
A strong method of authentication compared to other traditional
mechanisms.
7.2 Limitations
CHAPTER 8: CONCLUSION
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We have completed our project using java as our programming language and
NetBeans IDE. From the initial phase of the project till its completion we have
encountered a number of problems which were later eliminated. With continuous
effort finally the application was run successfully with all the test being a success.