The document summarizes iris recognition as a biometric technique for human identification. It discusses how iris recognition works in four main steps: iris image acquisition, preprocessing the image to locate and normalize the iris, extracting features from the iris pattern, and matching the features to stored iris patterns. The iris is suitable for recognition due its complex random patterns that are stable over a person's lifetime and differ even between identical twins. Iris recognition provides highly accurate identification with a very low false match rate of 1 in 1.2 million.
This document presents a student's proposal for a human retina identification system using biometric technology. The proposal discusses how the unique patterns of blood vessels in the retina can be used to identify individuals with high accuracy. The proposed system will involve segmenting retinal images to extract features like branch points and endpoints, and then storing these features as templates to compare new images against for matching. The student believes this technology provides strong security but also has disadvantages like intrusiveness and high costs that need to be addressed.
Retinal recognition uses the unique pattern of blood vessels in the retina to identify individuals. It is considered the most reliable biometric since the retina develops randomly and is difficult to alter. However, retinal scanners are invasive, expensive, and not widely accepted. They work by capturing an image of the retina using infrared light and extracting over 400 data points to create a template for identification. Factors like eye movement, distance from the lens, or a dirty lens can cause errors in scanning.
The document summarizes recent progress in iris recognition technology. It discusses iris image acquisition, preprocessing techniques like localization and normalization, and pattern recognition methods. It also outlines applications of iris recognition in areas like border control, criminal investigations, and secure banking. Emerging areas discussed include long-range iris recognition, multi-biometric systems, and generating synthetic iris images for database construction.
Retinal pattern recognition uses the unique pattern of veins beneath the retina to identify individuals. Researchers take digital images of the retina by projecting light into the eye and scanning the retina. While retinal scanning provides reliable identification, it requires a high level of user cooperation and the equipment is expensive, making it best suited to highly secure environments like prisons.
IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcsitconf
Biometrics has become important in security applications. In comparison with many other
biometric features, iris recognition has very high recognition accuracy because it depends on
iris which is located in a place that still stable throughout human life and the probability to find
two identical iris's is close to zero. The identification system consists of several stages including
segmentation stage which is the most serious and critical one. The current segmentation
methods still have limitation in localizing the iris due to circular shape consideration of the
pupil. In this research, Daugman method is done to investigate the segmentation techniques.
Eyelid detection is another step that has been included in this study as a part of segmentation
stage to localize the iris accurately and remove unwanted area that might be included. The
obtained iris region is encoded using haar wavelets to construct the iris code, which contains
the most discriminating feature in the iris pattern. Hamming distance is used for comparison of
iris templates in the recognition stage. The dataset which is used for the study is UBIRIS
database. A comparative study of different edge detector operator is performed. It is observed
that canny operator is best suited to extract most of the edges to generate the iris code for
comparison. Recognition rate of 89% and rejection rate of 95% is achieved.
The document discusses biometric identification methods using retinal scans and iris recognition. Retinal scans map the unique blood vessel patterns of the retina, which differ between individuals and remain stable over a person's lifetime. Iris recognition systems use active or passive methods to identify patterns in the iris and convert them to a mathematical code for identification. These biometric methods provide accurate, reliable identification and are used for security applications like prisons, ATMs, and verifying the identities of athletes. However, retinal scans have limitations like being invasive, affected by eye disease or trauma, and requiring user cooperation.
The document discusses iris biometrics for identification. It describes the retina and iris, noting that the unique patterns of blood vessels and iris are highly distinctive even between identical twins. Iris recognition involves using cameras to capture high-resolution photos of the iris within a few feet. Software then locates the iris boundaries, normalizes it, and encodes the pattern to generate an iris code for identification purposes by comparing to stored templates. The iris remains stable over a lifetime but can be affected by some eye diseases. Compared to other biometrics, iris scanning is accurate, stable, fast, and scalable for identification.
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 presents a student's proposal for a human retina identification system using biometric technology. The proposal discusses how the unique patterns of blood vessels in the retina can be used to identify individuals with high accuracy. The proposed system will involve segmenting retinal images to extract features like branch points and endpoints, and then storing these features as templates to compare new images against for matching. The student believes this technology provides strong security but also has disadvantages like intrusiveness and high costs that need to be addressed.
Retinal recognition uses the unique pattern of blood vessels in the retina to identify individuals. It is considered the most reliable biometric since the retina develops randomly and is difficult to alter. However, retinal scanners are invasive, expensive, and not widely accepted. They work by capturing an image of the retina using infrared light and extracting over 400 data points to create a template for identification. Factors like eye movement, distance from the lens, or a dirty lens can cause errors in scanning.
The document summarizes recent progress in iris recognition technology. It discusses iris image acquisition, preprocessing techniques like localization and normalization, and pattern recognition methods. It also outlines applications of iris recognition in areas like border control, criminal investigations, and secure banking. Emerging areas discussed include long-range iris recognition, multi-biometric systems, and generating synthetic iris images for database construction.
Retinal pattern recognition uses the unique pattern of veins beneath the retina to identify individuals. Researchers take digital images of the retina by projecting light into the eye and scanning the retina. While retinal scanning provides reliable identification, it requires a high level of user cooperation and the equipment is expensive, making it best suited to highly secure environments like prisons.
IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcsitconf
Biometrics has become important in security applications. In comparison with many other
biometric features, iris recognition has very high recognition accuracy because it depends on
iris which is located in a place that still stable throughout human life and the probability to find
two identical iris's is close to zero. The identification system consists of several stages including
segmentation stage which is the most serious and critical one. The current segmentation
methods still have limitation in localizing the iris due to circular shape consideration of the
pupil. In this research, Daugman method is done to investigate the segmentation techniques.
Eyelid detection is another step that has been included in this study as a part of segmentation
stage to localize the iris accurately and remove unwanted area that might be included. The
obtained iris region is encoded using haar wavelets to construct the iris code, which contains
the most discriminating feature in the iris pattern. Hamming distance is used for comparison of
iris templates in the recognition stage. The dataset which is used for the study is UBIRIS
database. A comparative study of different edge detector operator is performed. It is observed
that canny operator is best suited to extract most of the edges to generate the iris code for
comparison. Recognition rate of 89% and rejection rate of 95% is achieved.
The document discusses biometric identification methods using retinal scans and iris recognition. Retinal scans map the unique blood vessel patterns of the retina, which differ between individuals and remain stable over a person's lifetime. Iris recognition systems use active or passive methods to identify patterns in the iris and convert them to a mathematical code for identification. These biometric methods provide accurate, reliable identification and are used for security applications like prisons, ATMs, and verifying the identities of athletes. However, retinal scans have limitations like being invasive, affected by eye disease or trauma, and requiring user cooperation.
The document discusses iris biometrics for identification. It describes the retina and iris, noting that the unique patterns of blood vessels and iris are highly distinctive even between identical twins. Iris recognition involves using cameras to capture high-resolution photos of the iris within a few feet. Software then locates the iris boundaries, normalizes it, and encodes the pattern to generate an iris code for identification purposes by comparing to stored templates. The iris remains stable over a lifetime but can be affected by some eye diseases. Compared to other biometrics, iris scanning is accurate, stable, fast, and scalable for identification.
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.
Iris segmentation analysis using integro differential operator and hough tran...Nadeer Abu Jraerr
This document presents a study on iris segmentation analysis using the integro-differential operator and Hough transform techniques in biometric systems. The study experiments with two iris segmentation techniques: the integro-differential operator and Hough transform. The Hough transform technique segmented iris images more successfully than the integro-differential operator, achieving a segmentation accuracy of 80.88% compared to 22.06% for the integro-differential operator. The Hough transform also had lower false rejection and recognition error rates. However, it has higher computational complexity than the integro-differential operator. The document concludes that the Hough transform technique resulted in better overall performance than the integro-differential operator for iris segmentation
This document summarizes various methods for iris feature extraction that are used in iris recognition systems. It discusses four main categories of iris feature extraction techniques: texture-based, phase-based, zero-crossing based, and intensity variation based. It provides details on several popular methods, including Gabor filtering, Log-Gabor filtering, wavelet transforms, and Haar encoding. It also reviews several studies that have compared the performance of different iris feature extraction algorithms and their accuracy rates.
This document provides an overview of multimodal biometric systems. It discusses various biometric modalities including fingerprint, palm print, iris, face, and voice. For each modality, it describes the basic methodology, including enrollment and recognition processes. It also reviews literature on implementations of unimodal and multimodal biometric systems using these physiological and behavioral traits. The document concludes that multimodal biometric systems that fuse information from multiple traits can provide more robust and accurate person identification compared to single-trait unimodal systems.
This document provides information about retinal recognition as a biometric identification method. It discusses the anatomy of the retina and how retinal scanning works. Retinal recognition analyzes the unique blood vessel patterns in a person's retina. While very reliable, retinal recognition is also considered invasive and expensive. The document reviews the history and development of retinal scanning technology over time. It examines the strengths of retinal recognition in providing a large number of identification points and its stability over a person's lifetime. However, weaknesses include the need for user cooperation, discomfort of the scanning process, and the high cost of retinal scanning devices.
Secure Authentication System Using Video SurveillanceIOSR Journals
The document proposes a secure authentication system using video surveillance that extracts key features from video to identify and authenticate users. It begins with an introduction to existing biometric authentication methods and their limitations. It then describes the proposed video surveillance system which involves extracting foreground objects from video using background subtraction and shadow detection algorithms. Key features like skeleton and centroid features are extracted from the foreground objects using triangulation and depth first search. These features are stored in a database and used to identify and authenticate users by comparing with live video feed features. The system aims to provide secure authentication using video's ability to characterize biometrics based on dynamics like gait, while overcoming limitations of existing methods.
The document is a presentation on retina scanning given by Khushboo Shrivastava at Madhav Institute of Technology and Science. It discusses the structure of the eye, what retina scanning is, how it works, its history, principles, techniques, specifications, comparison to iris scanning, uses, advantages, disadvantages, applications, and future. Retina scanning uses the unique patterns of the retina's blood vessels to identify individuals and provide security authentication in under 2 seconds with very low false acceptance and rejection rates. However, it is an intrusive technique and accuracy can be affected by eye disease.
High Security Human Recognition System using Iris ImagesIDES Editor
In this paper, efficient biometric security
technique for Integer Wavelet Transform based Human
Recognition System (IWTHRS) using Iris images
verification is described. Human Recognition using Iris
images is one of the most secure and authentic among the
other biometrics. The Iris and Pupil boundaries of an Eye
are identified by Integro-Differential Operator. The features
of the normalized Iris are extracted using Integer Wavelet
Transform and Discrete Wavelet Transform. The Hamming
Distance is used for matching of two Iris feature vectors. It
is observed that the values of FAR, FRR, EER and
computation time required are improved in the case of
Integer Wavelet Transform based Human Recognition
System as compared to Discrete Wavelet Transform based
Human Recognition System (DWTHRS).
A Robust Approach in Iris Recognition for Person AuthenticationIOSR Journals
The document describes a robust approach for iris recognition used for person authentication. It proposes using eight main stages: 1) scanning the iris image, 2) converting it to grayscale, 3) applying median filters to reduce noise, 4) detecting the pupil center, 5) using canny edge detection to identify iris and pupil edges, 6) determining the iris and pupil radii, 7) localizing the iris, and 8) unrolling the iris texture. It then uses k-means clustering to compare images and match them to authenticate individuals in a database. The approach aims to improve on previous iris recognition methods by more accurately detecting non-circular iris and pupil shapes.
This document compares various biometric methods for identification and verification. It discusses fingerprint recognition, face recognition, voice recognition, and iris recognition as some of the main biometric techniques. For each method, it describes how the biometric data is captured and analyzed, the advantages and disadvantages, and examples of applications where the technique can be used. The document provides an overview of the history of biometrics and the typical modules involved in a biometric system, such as sensors, feature extraction, matching, and template databases.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
IRDO: Iris Recognition by fusion of DTCWT and OLBPIJERA Editor
This document proposes a new iris recognition method called IRDO that fuses Dual Tree Complex Wavelet Transform (DTCWT) and Overlapping Local Binary Pattern (OLBP) features. DTCWT is used to extract micro-texture features from the iris, while OLBP enhances the extraction of edge features. Fusing these two methods results in improved matching performance and classification accuracy compared to state-of-the-the-art techniques. The proposed IRDO method achieves higher iris recognition rates as measured by Total Success Rate and Equal Error Rate.
A comparison of multiple wavelet algorithms for iris recognition 2IAEME Publication
The document compares multiple wavelet algorithms for iris recognition, including complex wavelet transform, Gabor wavelets, and discrete wavelet transform. It first provides background on iris recognition and wavelets. It then describes typical iris recognition systems which involve image acquisition, segmentation, normalization, feature extraction, and matching. Next, it discusses complex wavelets, Gabor wavelets, and discrete wavelet transform for feature extraction in iris recognition. Complex wavelets extract phase and amplitude information to accurately describe oscillating functions. Gabor filters model human visual processing and generate phase-coded bit strings for matching. Discrete wavelet transform uses dyadic wavelet scales and positions for efficient analysis. The paper compares these wavelet algorithms for iris image enhancement,
This document summarizes a research paper on hand vein authentication systems. It discusses how hand vein patterns are unique biometric identifiers that can be used for authentication. The system works by capturing an image of the veins in the back of the hand, extracting the vein pattern features, and matching the features to authenticate a user. Key advantages of this approach are that vein patterns are difficult to replicate, located inside the hand, and stable over time. The document provides details on the image processing and authentication methodology.
This document proposes integrating iris recognition with RFID cards to develop a high-security access environment. It discusses:
1) How iris recognition works, including iris segmentation, normalization, feature extraction using wavelets, and identification by comparing templates.
2) Details of the RFID card used, including its microcontroller and memory, and the design of an RFID card programmer.
3) The proposed method of integrating iris recognition by storing the extracted iris features and a signature in the RFID card, and comparing them during authentication.
4) Preliminary test results comparing combinations of wavelet coefficients to find the best approach. Performance metrics like reading time, writing time, and memory utilization are evaluated.
Introduction
The term “biometrics” is derived from the Greek words “bio” (life) and “metrics” (to measure). Automated biometric systems have only become available over the last few decades, due to significant advances in the field of computer processing. Many of these new automated techniques, however, are based on ideas that were originally conceived hundreds, even thousands of years ago.
One of the oldest and most basic examples of a characteristic that is used for recognition by humans is the face. Since the beginning of civilization, humans have used faces to identify known (familiar) and unknown (unfamiliar) individuals. This simple task became increasingly more challenging as populations increased and as more convenient methods of travel introduced many new individuals into- once small communities. The concept of human-to-human recognition is also seen in behavioral-predominant biometrics such as speaker and gait recognition. Individuals use these characteristics, somewhat unconsciously, to recognize known individuals on a day-to-day basis.
This is a Fingerprint based class attendance system in higher institutions, The implementation take attendance of student in a class and give output of student eligibility status at the end of the semester or term
The paper explores iris recognition for personal identification and verification. In this paper a new iris recognition technique is proposed using (Scale Invariant Feature Transform) SIFT. Image-processing algorithms have been validated on noised real iris image database. The proposed innovative technique is computationally effective as well as reliable in terms of recognition rates.
This document discusses India transitioning to fully electronic voting. It introduces m-voting which allows voting via mobile phones. It describes the iris scanning process where voters' iris patterns are enrolled and then scanned for verification before allowing them to vote. The benefits of this system are that it saves time, prevents election malpractices, and enables secure voting for remote populations like military forces and those in remote areas.
Biometric Iris Recognition Based on Hybrid Techniqueijsc
This document presents a study on implementing an iris recognition system using a hybrid technique. The system utilizes several image processing and machine learning techniques. It begins with preprocessing the iris image, including capturing, resizing and converting to grayscale. Histogram equalization is then used for enhancement. Two-dimensional discrete wavelet transform (2D DWT) is applied for feature extraction. Various edge detection algorithms including Canny, Prewitt, Roberts and Sobel are used to detect iris boundaries. The features are then stored in a vector for classification. The system is tested on different iris images and analysis shows 2D DWT and Canny edge detection provide adequate results for feature extraction and iris recognition.
The document discusses iris recognition as a biometric authentication technique. It begins with an introduction to iris recognition and the eye. It then covers how iris recognition works, including image acquisition, processing, feature extraction, and matching. Applications of iris recognition include ATMs, airports, prisons, and online services. Iris recognition provides advantages such as highly complex and individual iris patterns that remain stable over a lifetime. It is also very resistant to false matches and imitation. The document concludes that iris recognition is an excellent and robust identification system.
This document discusses different biometric authentication techniques as alternatives to traditional passwords and PIN numbers. It describes several common biometric methods including fingerprint recognition, facial recognition, iris scanning, hand geometry, retina scanning, signature dynamics, and speaker recognition. For each method, it provides a brief explanation of how the biometric works and potential applications. The document promotes biometric authentication as a more secure option that eliminates the problem of forgotten passwords or PINs.
Iris segmentation analysis using integro differential operator and hough tran...Nadeer Abu Jraerr
This document presents a study on iris segmentation analysis using the integro-differential operator and Hough transform techniques in biometric systems. The study experiments with two iris segmentation techniques: the integro-differential operator and Hough transform. The Hough transform technique segmented iris images more successfully than the integro-differential operator, achieving a segmentation accuracy of 80.88% compared to 22.06% for the integro-differential operator. The Hough transform also had lower false rejection and recognition error rates. However, it has higher computational complexity than the integro-differential operator. The document concludes that the Hough transform technique resulted in better overall performance than the integro-differential operator for iris segmentation
This document summarizes various methods for iris feature extraction that are used in iris recognition systems. It discusses four main categories of iris feature extraction techniques: texture-based, phase-based, zero-crossing based, and intensity variation based. It provides details on several popular methods, including Gabor filtering, Log-Gabor filtering, wavelet transforms, and Haar encoding. It also reviews several studies that have compared the performance of different iris feature extraction algorithms and their accuracy rates.
This document provides an overview of multimodal biometric systems. It discusses various biometric modalities including fingerprint, palm print, iris, face, and voice. For each modality, it describes the basic methodology, including enrollment and recognition processes. It also reviews literature on implementations of unimodal and multimodal biometric systems using these physiological and behavioral traits. The document concludes that multimodal biometric systems that fuse information from multiple traits can provide more robust and accurate person identification compared to single-trait unimodal systems.
This document provides information about retinal recognition as a biometric identification method. It discusses the anatomy of the retina and how retinal scanning works. Retinal recognition analyzes the unique blood vessel patterns in a person's retina. While very reliable, retinal recognition is also considered invasive and expensive. The document reviews the history and development of retinal scanning technology over time. It examines the strengths of retinal recognition in providing a large number of identification points and its stability over a person's lifetime. However, weaknesses include the need for user cooperation, discomfort of the scanning process, and the high cost of retinal scanning devices.
Secure Authentication System Using Video SurveillanceIOSR Journals
The document proposes a secure authentication system using video surveillance that extracts key features from video to identify and authenticate users. It begins with an introduction to existing biometric authentication methods and their limitations. It then describes the proposed video surveillance system which involves extracting foreground objects from video using background subtraction and shadow detection algorithms. Key features like skeleton and centroid features are extracted from the foreground objects using triangulation and depth first search. These features are stored in a database and used to identify and authenticate users by comparing with live video feed features. The system aims to provide secure authentication using video's ability to characterize biometrics based on dynamics like gait, while overcoming limitations of existing methods.
The document is a presentation on retina scanning given by Khushboo Shrivastava at Madhav Institute of Technology and Science. It discusses the structure of the eye, what retina scanning is, how it works, its history, principles, techniques, specifications, comparison to iris scanning, uses, advantages, disadvantages, applications, and future. Retina scanning uses the unique patterns of the retina's blood vessels to identify individuals and provide security authentication in under 2 seconds with very low false acceptance and rejection rates. However, it is an intrusive technique and accuracy can be affected by eye disease.
High Security Human Recognition System using Iris ImagesIDES Editor
In this paper, efficient biometric security
technique for Integer Wavelet Transform based Human
Recognition System (IWTHRS) using Iris images
verification is described. Human Recognition using Iris
images is one of the most secure and authentic among the
other biometrics. The Iris and Pupil boundaries of an Eye
are identified by Integro-Differential Operator. The features
of the normalized Iris are extracted using Integer Wavelet
Transform and Discrete Wavelet Transform. The Hamming
Distance is used for matching of two Iris feature vectors. It
is observed that the values of FAR, FRR, EER and
computation time required are improved in the case of
Integer Wavelet Transform based Human Recognition
System as compared to Discrete Wavelet Transform based
Human Recognition System (DWTHRS).
A Robust Approach in Iris Recognition for Person AuthenticationIOSR Journals
The document describes a robust approach for iris recognition used for person authentication. It proposes using eight main stages: 1) scanning the iris image, 2) converting it to grayscale, 3) applying median filters to reduce noise, 4) detecting the pupil center, 5) using canny edge detection to identify iris and pupil edges, 6) determining the iris and pupil radii, 7) localizing the iris, and 8) unrolling the iris texture. It then uses k-means clustering to compare images and match them to authenticate individuals in a database. The approach aims to improve on previous iris recognition methods by more accurately detecting non-circular iris and pupil shapes.
This document compares various biometric methods for identification and verification. It discusses fingerprint recognition, face recognition, voice recognition, and iris recognition as some of the main biometric techniques. For each method, it describes how the biometric data is captured and analyzed, the advantages and disadvantages, and examples of applications where the technique can be used. The document provides an overview of the history of biometrics and the typical modules involved in a biometric system, such as sensors, feature extraction, matching, and template databases.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
IRDO: Iris Recognition by fusion of DTCWT and OLBPIJERA Editor
This document proposes a new iris recognition method called IRDO that fuses Dual Tree Complex Wavelet Transform (DTCWT) and Overlapping Local Binary Pattern (OLBP) features. DTCWT is used to extract micro-texture features from the iris, while OLBP enhances the extraction of edge features. Fusing these two methods results in improved matching performance and classification accuracy compared to state-of-the-the-art techniques. The proposed IRDO method achieves higher iris recognition rates as measured by Total Success Rate and Equal Error Rate.
A comparison of multiple wavelet algorithms for iris recognition 2IAEME Publication
The document compares multiple wavelet algorithms for iris recognition, including complex wavelet transform, Gabor wavelets, and discrete wavelet transform. It first provides background on iris recognition and wavelets. It then describes typical iris recognition systems which involve image acquisition, segmentation, normalization, feature extraction, and matching. Next, it discusses complex wavelets, Gabor wavelets, and discrete wavelet transform for feature extraction in iris recognition. Complex wavelets extract phase and amplitude information to accurately describe oscillating functions. Gabor filters model human visual processing and generate phase-coded bit strings for matching. Discrete wavelet transform uses dyadic wavelet scales and positions for efficient analysis. The paper compares these wavelet algorithms for iris image enhancement,
This document summarizes a research paper on hand vein authentication systems. It discusses how hand vein patterns are unique biometric identifiers that can be used for authentication. The system works by capturing an image of the veins in the back of the hand, extracting the vein pattern features, and matching the features to authenticate a user. Key advantages of this approach are that vein patterns are difficult to replicate, located inside the hand, and stable over time. The document provides details on the image processing and authentication methodology.
This document proposes integrating iris recognition with RFID cards to develop a high-security access environment. It discusses:
1) How iris recognition works, including iris segmentation, normalization, feature extraction using wavelets, and identification by comparing templates.
2) Details of the RFID card used, including its microcontroller and memory, and the design of an RFID card programmer.
3) The proposed method of integrating iris recognition by storing the extracted iris features and a signature in the RFID card, and comparing them during authentication.
4) Preliminary test results comparing combinations of wavelet coefficients to find the best approach. Performance metrics like reading time, writing time, and memory utilization are evaluated.
Introduction
The term “biometrics” is derived from the Greek words “bio” (life) and “metrics” (to measure). Automated biometric systems have only become available over the last few decades, due to significant advances in the field of computer processing. Many of these new automated techniques, however, are based on ideas that were originally conceived hundreds, even thousands of years ago.
One of the oldest and most basic examples of a characteristic that is used for recognition by humans is the face. Since the beginning of civilization, humans have used faces to identify known (familiar) and unknown (unfamiliar) individuals. This simple task became increasingly more challenging as populations increased and as more convenient methods of travel introduced many new individuals into- once small communities. The concept of human-to-human recognition is also seen in behavioral-predominant biometrics such as speaker and gait recognition. Individuals use these characteristics, somewhat unconsciously, to recognize known individuals on a day-to-day basis.
This is a Fingerprint based class attendance system in higher institutions, The implementation take attendance of student in a class and give output of student eligibility status at the end of the semester or term
The paper explores iris recognition for personal identification and verification. In this paper a new iris recognition technique is proposed using (Scale Invariant Feature Transform) SIFT. Image-processing algorithms have been validated on noised real iris image database. The proposed innovative technique is computationally effective as well as reliable in terms of recognition rates.
This document discusses India transitioning to fully electronic voting. It introduces m-voting which allows voting via mobile phones. It describes the iris scanning process where voters' iris patterns are enrolled and then scanned for verification before allowing them to vote. The benefits of this system are that it saves time, prevents election malpractices, and enables secure voting for remote populations like military forces and those in remote areas.
Biometric Iris Recognition Based on Hybrid Techniqueijsc
This document presents a study on implementing an iris recognition system using a hybrid technique. The system utilizes several image processing and machine learning techniques. It begins with preprocessing the iris image, including capturing, resizing and converting to grayscale. Histogram equalization is then used for enhancement. Two-dimensional discrete wavelet transform (2D DWT) is applied for feature extraction. Various edge detection algorithms including Canny, Prewitt, Roberts and Sobel are used to detect iris boundaries. The features are then stored in a vector for classification. The system is tested on different iris images and analysis shows 2D DWT and Canny edge detection provide adequate results for feature extraction and iris recognition.
The document discusses iris recognition as a biometric authentication technique. It begins with an introduction to iris recognition and the eye. It then covers how iris recognition works, including image acquisition, processing, feature extraction, and matching. Applications of iris recognition include ATMs, airports, prisons, and online services. Iris recognition provides advantages such as highly complex and individual iris patterns that remain stable over a lifetime. It is also very resistant to false matches and imitation. The document concludes that iris recognition is an excellent and robust identification system.
This document discusses different biometric authentication techniques as alternatives to traditional passwords and PIN numbers. It describes several common biometric methods including fingerprint recognition, facial recognition, iris scanning, hand geometry, retina scanning, signature dynamics, and speaker recognition. For each method, it provides a brief explanation of how the biometric works and potential applications. The document promotes biometric authentication as a more secure option that eliminates the problem of forgotten passwords or PINs.
Dr. Ahmed El-Feqi has extensive academic qualifications, including a Ph.D in Economics from the University of East Anglia. He has received fellowships from both the Ford Foundation and International Fellowship Programme in the United States. El-Feqi is currently a teaching assistant at Delta University. The document goes on to define key economic concepts like scarcity, resources, wants and needs, goods and services. It explains how economies must make choices due to limited resources, and uses models like the production possibilities frontier to illustrate opportunity costs and trade-offs.
This presentation discusses biometric authentication methods for enhancing security. It covers phases of biometric systems including capture, extraction, comparison and match/no match. Fingerprint recognition is described as the oldest method dating back to 1896 and widely used for criminal identification. The presentation also discusses other biometric techniques like hand geometry recognition, facial recognition analyzing attributes like eye sockets and mouth, voice recognition using formants, iris recognition using unique iris patterns, and emerging biometrics like vein scans, facial thermography, gait recognition, blood pulse, ear shape recognition and odor sensing. Biometric technologies can achieve e-commerce and e-government promises through strong personal authentication and each technique's performance varies by usage and environment.
Biometric Security advantages and disadvantagesPrabh Jeet
Biometrics refers to authentication techniques that rely on measurable physiological and individual characteristics to automatically verify identity. A biometric system uses behavioral or biological traits like fingerprints, iris scans, or voice to identify or verify individuals. Identification involves searching a biometric sample against a database of templates, while verification compares a sample to a single stored template. Biometrics are increasingly used for security applications like access control and transactions due to their convenience and effectiveness compared to traditional authentication methods.
The document defines biometrics as the automatic identification of a person based on physiological or behavioral characteristics. It lists different biometric characteristics including fingerprint, facial recognition, hand geometry, iris scan, and retina scan. It then describes several biometric recognition techniques such as fingerprint recognition, facial recognition, hand geometry, iris recognition, and retina recognition. Finally, it discusses applications of biometrics such as preventing unauthorized access, criminal identification, and improving security in areas like ATMs, cellphones, computers, automobiles, and airports.
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.
The rich texture of the iris can be used as a biometric cue for person recognition.
This variability in Iris texture is due to the accumulation of multiple anatomical entities composing its structure.
Even the left iris and the right iris of an individual exhibit significant differences in their texture, although some global similarities may be observed.
Due to the presence of distinctive information at multiple scales a wavelet-based signal processing approach is commonly used to extract features
from the iris
This document is a seminar report on an iris recognition biometric security system. It provides an abstract that describes iris recognition technology and how it is used for biometric identification. It then discusses the key components of an iris recognition system, including image acquisition, preprocessing, image analysis, and image recognition. It also compares iris recognition to other biometric technologies and discusses applications of iris recognition systems.
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.
This document discusses iris recognition systems. It begins by describing the three categories that iris recognition systems can be classified into: appearance based, texture based, and feature based extraction. It then discusses the typical components of an iris recognition system: iris image localization, feature extraction, and matching. The document goes on to describe the advantages of iris as a biometric trait, the modules involved in iris recognition including acquisition, segmentation, normalization, and encoding/matching. It provides details on each of these modules.
Software Implementation of Iris Recognition System using MATLABijtsrd
The software implementation of iris recognition system introduces in this paper. This system intends to apply for high security required areas. The demand on security is increasing greatly in these years and biometric recognition gradually becomes a hot field of research. Iris recognition is a branch of biometric recognition method. In thesis, Iris recognition system consists of localization of the iris region and generation of data set of iris images followed by iris pattern recognition. In thesis, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network NN is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The implementation of the system is developed with MATLAB. The results of simulations illustrate the effectiveness of the neural system in personal identification. Finally, the accuracy of iris recognition system is tested and evaluated with different iris images. Mo Mo Myint Wai | Nyan Phyo Aung | Lwin Lwin Htay "Software Implementation of Iris Recognition System using MATLAB" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25258.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25258/software-implementation-of-iris-recognition-system-using-matlab/mo-mo-myint-wai
This document presents a new iris segmentation method for iris recognition systems. The proposed method uses Canny edge detection and Hough transform to locate the iris boundary after finding the pupil boundary using image gray levels. Experiments on the CASIA iris image database of 756 images show the method can accurately detect the iris boundary in 99.2% of images. This is an improvement over other existing segmentation techniques. The key steps of the proposed method are preprocessing, segmentation using Canny edge detection and Hough transform, normalization using the rubber sheet model, feature encoding with Gabor wavelets, and matching with Hamming distance.
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.
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...IJNSA Journal
Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the
security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed
systems, make it a good candidate to replace most of thesecurity systems around. By making use of the
distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person.
Identification of this person is possible by applying appropriate matching algorithm.In this paper,
Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical
analysis of different feature detection operators is performed, features extracted is encoded using Haar
wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on
the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of
the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and
False Reject Rate is 10%.
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...IJNSA Journal
Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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.
This document describes a technique for human iris recognition for biometric identification. It involves 6 major steps: image acquisition, localization, isolation, normalization, feature extraction, and matching. The iris is localized by detecting the pupil and outer iris boundaries using techniques like Canny edge detection and Hough transforms. The iris region is then isolated using masking. It is normalized and represented as a fixed-sized block. Features are extracted using techniques like Gabor filters and Haar wavelets to generate biometric templates. Templates are matched using Hamming distance to identify individuals in applications like border control, computer login, and financial transactions. The iris has properties that make it suitable and accurate for identification compared to other biometrics.
The document summarizes iris recognition as a biometric authentication method. It discusses how the iris is unique and stable over time, making it suitable for identification. The basic technique involves capturing an eye image, localizing the iris, and encoding patterns in the iris to generate a code for matching. Advantages include speed, accuracy and the iris being difficult to forge. Current applications include smartphone unlocking and border security. Future uses could help identify patients in hospitals and provide officer safety during traffic stops.
The document summarizes iris recognition as a biometric authentication method. It discusses how the iris is unique and stable over time, making it suitable for identification. The technique involves capturing an iris image, localizing the iris region, and encoding the iris pattern into a code for matching. Iris recognition provides accurate and fast authentication and has applications in security systems, border control, and other areas where secure identification is needed. However, it requires user cooperation and specialized cameras. The document also outlines current and potential future uses of iris recognition technology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Literature Review on Iris Segmentation Techniques for Iris Recognition SystemsIOSR Journals
This document reviews various techniques for iris segmentation in iris recognition systems. It discusses 8 techniques: (1) Integrodifferential operator, (2) Hough transform, (3) Masek method, (4) Fuzzy clustering algorithm, (5) Pulling and Pushing method, (6) Eight-neighbor connection based clustering, (7) Segmentation approach based on Fourier spectral density, and (8) Circular Gabor Filter. Each technique achieves some level of segmentation accuracy but also has disadvantages like high computational time, low accuracy, or poor performance on noisy images. The document concludes that a unified framework approach provides the highest overall segmentation accuracy for robustly segmenting iris images.
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
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Power Grid Model
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
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The Inbuilt Password Iris
1. The Inbuilt Password: Iris
Swarup S. Kulkarni Madhuri A. Undre
MCA-II, Department of Computer Engineering. MCA-II, Department of Computer Engineering.
Vishwakarma Institute of Technology, Vishwakarma Institute of Technology,
Pune, India. Pune, India.
kulkarniswarup@ymail.com maundre@yahoo.com
Abstract— Iris recognition is a reliable approach to human The process of iris recognition is composed of four steps:
identification. It has received increasing attention in recent years.
The uniqueness and randomness of human iris patterns enable us 1. Iris image acquisition.
to use it as quicker, easier and highly reliable forms of automatic 2. Preprocessing of the image by locating the iris,
human identification, where the human iris serves as a type of normalizing the iris and enhancing the image.
biological passport, PIN or password. The structure of human
iris is so complex and unique that even genetically identical eyes, 3. Extracting the local features of the iris.
for example from twins or in the probable future, from human
clones, have different iris patterns. This paper tells about how the 4. Matching the iris-pattern with an already stored
iris recognition system works. Iris recognition system comprises iris-pattern.
of iris image acquisition, image preprocessing, feature extraction This paper, first gives a short introduction to the properties
and pattern matching. of the eye, and then goes the four steps of iris recognition as
well as some applications and advantages over other
techniques.
Keywords-Biometrics, iris recognition, enrollment,
identification .
I. INTRODUCTION II. THE EYE
Today in our society we have a lot of situations where we
need to identify a person. Traditionally, the ways of identifying
a person has been based on what the person knows, like
passwords, of what the person possesses: ID cards, keys,
frequency built activators (parking door openers, etc.) and so
on. None of these methods are secure. What a person possesses
might easily be lost or stolen, and what a person knows can be
forgotten, or stolen by someone else. Because of this, there has
been a growing demand of other ways to identify persons, and
scientists have started to explore identification based on
biometric features.
Figure 1: The eye
Biometrics is an emerging field of technology using unique
and measurable physical, biological, or behavioral The colored part of the eye is called the iris. It controls
characteristics that can be processed to identify a person. light levels inside the eye similar to the aperture on a
Biometric features are divided in two categories: behavioral, camera. The round opening in the center of the iris is called the
like voice recognition and handwriting, and physiological, like pupil. The iris is embedded with tiny muscles that dilate
iris, retina, face, and DNA and fingerprint recognition. Among (widen) and constrict (narrow) the pupil size.
all of these, the one of most secure method to identify a person The iris’s random patterns are unique to each individual —
is the iris recognition. a human “bar code” or living passport. No two irises are alike.
Iris recognition was proposed as early as 1936 by Each person has a distinct pattern of filaments, pits and
ophthalmologist Frank Burch, but it took more than 50 years to striations in the colored rings surrounding the pupil of each
finally start researching it seriously, and first in 1994, Dr. John eye. In this pattern, scientists have identified about 250 degrees
Daugman patented a set of algorithms that makes up the basis of freedom that is 250 non-related unique features of a person’s
of all current iris recognition system. iris. The following images demonstrate the variations found in
iris:
2. B. Preprocessing of the image
1) Iris localization
Image acquisition of the iris cannot be expected to yield an
image containing only the iris. It will also contain data derived
from the surrounding eye region. Therefore, prior to iris pattern
matching, it is important to localize that portion of the image
derived from inside the limbus (the border between the sclera
and the iris) and outside the pupil. If the eyelids are occluding
part of the iris, then only that portion of the image without the
eyelids should be included.
Figure 2: Different iris
III. IRIS RECOGNITION SYSTEM
Design and implementation of a system for iris recognition
can be subdivided into three parts. The first part relates to
image acquisition. The second part is concerned with localizing
the iris from a captured image. The third part is concerned with
matching an extracted iris pattern with candidate data base
entries.
Figure 4: Localization of the iris
The task of locating the iris consists of locating the inner
and outer boundaries of the iris. Both of these boundaries are
circular, the problem lies in the fact that they are not co-centric.
Very often the pupil center is nasal, and inferior, to the iris
center. Its radius can be displaced as much as 15% towards the
nose from the center of the iris. This means that the outer and
inner boundaries must be calculated separately, as two
independent circles.
2) Normalization
Now we have the iris located, but still one image of an iris
normally will be very different from another image of the same
iris. This might be for many reasons:
• Size of the image, depending of the distance from the
Figure 3: Schematic diagram of iris recognition. camera.
There are two phases in process of recognition - enrollment • Size of the pupil. The pupil varies with intensity of
and authentication. Enrollment is the first time when the iris of light, and so might stretch or compress the iris tissue,
subject is scanned and stored in template database for future interfering with matching of iris patterns.
identification purpose. Authentication is the process in which
iris of subject is scanned and compared with the sample in the • The orientation of the iris, depending upon the head
database. tilt, torsion eye rotation within its socket, and camera
angles.
A. Iris Acquisition As well, it is necessary to truncate the boundary zones of
The first step is location of the iris by a dedicated camera the iris. This is because what we have is the iris localized
no more than 3 feet from the eye. After the camera situates the between two perfect circles, and as it turns out, the pupil is not
eye, the algorithm narrows in from the right and left of the iris a perfect circle. The outer boundary between the iris and the
to locate its outer edge. This horizontal approach accounts for sclera sometimes is erroneous as a result of the subject using
obstruction caused by the eyelids. It simultaneously locates the contact lenses. To normalize the representation of the iris, the
inner edge of the iris (at the pupil), excluding the lower 90° image of the iris is converted from Cartesian to doubly
because of inherent moisture and lighting issues. dimensionless polar reference form.
3. D. Matching the iris-pattern with stored one:
After iris localization, the final step is pattern matching of
the iris image with other images from the database. First the
iris image of the person to be identified is captured and then
compared with the images existing in the database. After
localizing and aligning the image containing the iris, the next
task is to decide if this pattern matches with the one existing in
Figure 5: Transformation from Cartesian to polar reference the database. A particular user is declared as authenticated and
form. valid only if a match is found.
3) Enhancement of images To perform the recognition, two IrisCodes are compared.
The image we are working with is not optimally sharp in The amount of difference between two IrisCodes - Hamming
contrast, and might have a non-uniform illumination because of Distance (HD) is used as the test of statistical independence
the positioning of the light source when the image was between the two IrisCodes. If the HD indicates one third of the
captured. It is therefore necessary to enhance the image; iris codes are different, the IrisCodes fail the test of statistical
otherwise reading of the iris pattern might not be successful. independence indicating that the IrisCodes are of the same iris.
The enhancements consist of sharpening the picture with a Therefore the key concept to iris recognition is failure of test of
sharpening mask, and reduce the effect of non-uniform statistical independence.
illumination.
IV. APPLICATIONS
Iris-scan technology has been piloted in ATM
environments in England, the US, Japan and Germany since as
early as 1997. In these pilots the customer’s iris data became
the verification tool for access to the bank account, thereby
eliminating the need for the customer to enter a PIN number or
Figure 6: Texture image before enhancement.
password. When the customer presented their eyeball to the
ATM machine and the identity verification was positive, access
was allowed to the bank account. These applications were very
successful and eliminated the concern over forgotten or stolen
passwords and received tremendously high customer approval
ratings.
Airports have begun to use iris-scanning for such diverse
Figure 7: Texture image after enhancement. functions as employee identification/verification for movement
through secure areas and allowing registered frequent airline
C. Features Extraction passengers a system that enables fast and easy identity
The one of the important step in extracting is generating iris verification in order to expedite their path through passport
code. Iris recognition technology converts the visible control.
characteristics into a 512 byte Iris Code, a template stored for
future identification attempts. From the iris' 11mm diameter, Other applications include monitoring prison transfers and
Dr. Daugman's algorithms provide 3.4 bits of data per square releases, as well as projects designed to authenticate on-line
mm. This density of information is such that each iris can be purchasing, on-line banking, on-line voting and on-line stock
said to have 266 'degrees of freedom'. This '266' measurement trading to name just a few. Iris-scan offers a high level of user
is cited in most iris recognition literature; after allowing for the security, privacy and general peace of mind for the consumer.
algorithm's correlative functions and for characteristics A highly accurate technology such as iris-scan has vast
inherent to most human eyes, Dr. Daugman concludes that 173 appeal because the inherent argument for any biometric is, of
"independent binary degrees-of-freedom" can be extracted course, increased security.
from his algorithm. A key differentiator of iris-scan technology
is the fact that 512 byte templates are generated for every iris, V. COMPARISON WITH OTHER TECHNIQUES
which facilitates match speed (capable of matching over
500,000 templates per second). A. Face Recognition:
It is an oldest recognition method and it is best done by
human. Light intensity, age, glasses, hairstyle, beard shape and
face covering mask may change verification success rate.
B. Fingerprint Recognition
One of the most well-known biometric. Unique for every
human, even twins. But it is not as accurate as iris recognition.
Figure 8: IrisCode generation
4. False accept rate is approximately one in one lack. While iris impossibility of surgically modifying without the risk of vision,
recognition false accept rate is one in 1.2 million. physiological response to light that is unique (which makes
sure that the eye that is scanned belongs to a living person),
C. Voice Recognition permanent features and ease of registering its image at some
It identifies speaker from short utterance. The main threat distance.
in this system is the valiant imitate voice can deceive
equipment. So it appears to be a weak biometric technology. VIII. REFERENCES:
1. www.sciencedirect.com
D. DNA
2. Dr. Mustafa H. Dahshan, Computer Engineering
DNA is unique to every human, and can be obtained from Department, College of Computer & Information
many sources. Also it doesn’t change through the life. But Sciences, King Saud University.
there are some difficulties using this techniques viz. DNA are
identical in twins, contamination can cause failure of test, 3. John Daugman. “How Iris Recognition Works”, IEEE
cannot be implemented in real time. Transaction paper, Vol. 14, JANUARY 2004
Hence iris recognition technique proves to be a most 4. www.wikipedia.com
accurate and suitable biometric technique.
5. IRIS Recognition,
www.cl.cam.ac.uk/~jgd1000/iris_recognition.html
VI. ADVANTAGES
6. Biometrics, scgwww.epfl.ch/courses/Biometrics-
• The iris is a thin membrane on the interior of the Lectures-2007-2008-pdf/07-Biometrics-Lecture-7-
eyeball. Iris patterns are extremely complex. Part2-2-2007-11-05.pdf
• Patterns are individual (even in fraternal or identical 7. www.biometrics.gov/Documents/IrisRec.pdf
twins).
8. Biometrics the Ultimate Reference by John D.
• Patterns are formed by six months after birth, stable Woodward-Jr., Nicholas M. Orlans, Peter T. Higgins.
after a year. They remain the same for life. Dreamtech press.
• Works even though the subject uses sunglasses or
contact lenses.
• Imitation is almost impossible.
• Patterns are easy to capture and encode.
• Very resistant to false matching (1/1.2 million) and
fraud.
TABLE I. COMPARISON OF TECHNIQUES
Techniques Misidentification Security Applications
rate
Iris 1/1,200,000 High High-security
Recognition
Fingerprinting 1/1,000 Medium Universal
Hand Shape 1/700 Low Low-security
facilities
Facial 1/100 Low Low-security
Recognition facilities
Signature 1/100 Low Low-security
facilities
Voice printing 1/30 Low Telephone service
VII. CONCLUSION
The iris is one of the most unique, data rich physical
structure on the human body, and one of the most robust ways
to identify humans. It works even though the subject uses
sunglasses or contact lenses. The properties of the iris that
enhance its suitability for use in automatic identification
includes its natural protection from the external environment,