This document summarizes an article that presents two methods for iris segmentation: 1) An automatic segmentation method that uses circular Hough transform to locate the iris boundaries and normalize the iris region using rubber sheet modeling. 2) A window technique that first finds the pupil's center and then detects edges on imaginary lines passing through the pupil center to segment the iris boundaries. Experiments showed the automatic method achieved 84% accuracy for pupil boundary detection while the window technique achieved 99% accuracy for pupil boundary detection and 79% accuracy for iris edge detection.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Journal looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
A robust iris recognition method on adverse conditionsijcseit
As a stable biometric system, iris has recently attracted great attention among the researchers. However,
research is still needed to provide appropriate solutions to ensure the resistance of the system against error
factors. The present study has tried to apply a mask to the image so that the unexpected factors affecting
the location of the iris can be removed. So, pupil localization will be faster and robust. Then to locate the
exact location of the iris, a simple stage of boundary displacement due to the Canny edge detector has been
applied. Then, with searching left and right IRIS edge point, outer radios of IRIS will be detect. Through
the process of extracting the iris features, it has been sought to obtain the distinctive iris texture features by
using a discrete stationary wavelets transform 2-D (DSWT2). Using DSWT2 tool and symlet 4 wavelet,
distinctive features are extracted. To reduce the computational cost, the features obtained from the
application of the wavelet have been investigated and a feature selection procedure, using similarity
criteria, has been implemented. Finally, the iris matching has been performed using a semi-correlation
criterion. The accuracy of the proposed method for localization on CASIA-v1, CASIA-v3 is 99.73%,
98.24% and 97.04%, respectively. The accuracy of the feature extraction proposed method for CASIA3 iris
images database is 97.82%, which confirms the efficiency of the proposed method.
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project’s dataset and the success percentage was found
to be 96%.
The optic disc (OD) is one of the important part of the eye for detecting various diseases such as Diabetic Retinopathy and Glaucoma. The localization of optic disc is extremely important for determining hard exudates and lesions. Diagnosis of the disease can prevent people from vision loss. This paper analyzes various techniques which are proposed by different authors for the exact localization of optic disc to prevent vision loss.
Model User Calibration Free Remote Gaze Estimation SystemKalle
Gaze estimation systems use calibration procedures that require active subject participation to estimate the point-of-gaze accurately. In these procedures, subjects are required to fixate on a specific point or points in space at specific time instances. This
paper describes a gaze estimation system that does not use calibration procedures that require active user participation. The
system estimates the optical axes of both eyes using images from a stereo pair of video cameras without a personal calibration procedure. To estimate the point-of-gaze, which lies along the visual axis, the angles between the optical and visual axes are estimated by a novel automatic procedure that minimizes the distance between the intersections of the visual axes of the left and right eyes with the surface of a display while subjects look naturally at the display (e.g., watching a video clip). Experiments
with four subjects demonstrate that the RMS error of this point-of-gaze estimation system is 1.3º.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Journal looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
A robust iris recognition method on adverse conditionsijcseit
As a stable biometric system, iris has recently attracted great attention among the researchers. However,
research is still needed to provide appropriate solutions to ensure the resistance of the system against error
factors. The present study has tried to apply a mask to the image so that the unexpected factors affecting
the location of the iris can be removed. So, pupil localization will be faster and robust. Then to locate the
exact location of the iris, a simple stage of boundary displacement due to the Canny edge detector has been
applied. Then, with searching left and right IRIS edge point, outer radios of IRIS will be detect. Through
the process of extracting the iris features, it has been sought to obtain the distinctive iris texture features by
using a discrete stationary wavelets transform 2-D (DSWT2). Using DSWT2 tool and symlet 4 wavelet,
distinctive features are extracted. To reduce the computational cost, the features obtained from the
application of the wavelet have been investigated and a feature selection procedure, using similarity
criteria, has been implemented. Finally, the iris matching has been performed using a semi-correlation
criterion. The accuracy of the proposed method for localization on CASIA-v1, CASIA-v3 is 99.73%,
98.24% and 97.04%, respectively. The accuracy of the feature extraction proposed method for CASIA3 iris
images database is 97.82%, which confirms the efficiency of the proposed method.
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC IN THE RETINAijait
The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other
than OD are blurred using 2D circular convolution. On applying mathematical steps like peak classification, concentric circles design and image difference calculation, OD is detected. The proposed method was evaluated using a subset of the STARE project’s dataset and the success percentage was found
to be 96%.
The optic disc (OD) is one of the important part of the eye for detecting various diseases such as Diabetic Retinopathy and Glaucoma. The localization of optic disc is extremely important for determining hard exudates and lesions. Diagnosis of the disease can prevent people from vision loss. This paper analyzes various techniques which are proposed by different authors for the exact localization of optic disc to prevent vision loss.
Model User Calibration Free Remote Gaze Estimation SystemKalle
Gaze estimation systems use calibration procedures that require active subject participation to estimate the point-of-gaze accurately. In these procedures, subjects are required to fixate on a specific point or points in space at specific time instances. This
paper describes a gaze estimation system that does not use calibration procedures that require active user participation. The
system estimates the optical axes of both eyes using images from a stereo pair of video cameras without a personal calibration procedure. To estimate the point-of-gaze, which lies along the visual axis, the angles between the optical and visual axes are estimated by a novel automatic procedure that minimizes the distance between the intersections of the visual axes of the left and right eyes with the surface of a display while subjects look naturally at the display (e.g., watching a video clip). Experiments
with four subjects demonstrate that the RMS error of this point-of-gaze estimation system is 1.3º.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
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.
This paper compares two different techniques of iris recognition and explains the steps of extracting iris from eye image palette formation and conditioning of palette for matching. The focus of the paper is in finding the suitable method for iris recognition on the basis of recognition time, recognition rate, false detection rate, conditioning time, algorithm complexity, bulk detection, database handling. In this paper comparison has been performed two methods feature based and phase based recognition.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
MULTI SCALE ICA BASED IRIS RECOGNITION USING BSIF AND HOG sipij
Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person
effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris
Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation
(HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and
bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with
iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is
obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database
image with test image final features to compute performance parameters. It is observed that the
performance of the proposed method is better compared to existing methods.
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Proposition of local automatic algorithm for landmark detection in 3D cephalo...journalBEEI
This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.
Reeb Graph for Automatic 3D CephalometryCSCJournals
The purpose of this study is to present a method of three-dimensional computed tomographic (3D-CT) cephalometrics and its use to study cranio/maxilla-facial malformations. We propose a system for automatic localization of cephalometric landmarks using reeb graphs. Volumetric images of a patient were reconstructed into 3D mesh. The proposed method is carried out in three steps: we begin by applying 3d mesh skull simplification, this mesh was reconstructed from a head volumetric medical image, and then we extract a reeb graph. Reeb graph mesh extraction represents a skeleton composed in a number of nodes and arcs. We are interested in the node position; we noted that some reeb nodes could be considered as cephalometric landmarks under specific conditions. The third step is to identify these nodes automatically by using elastic mesh registration using “thin plate” transformation and clustering. Preliminary results show a landmarks recognition rate of more than 90%, very close to the manually provided landmarks positions made by a medical stuff.
A New Approach of Iris Detection and RecognitionIJECEIAES
This paper proposes an IRIS recognition and detection model for measuring the e-security. This proposed model consists of the following blocks: segmentation and normalization, feature encoding and feature extraction, and classification. In first phase, histogram equalization and canny edge detection is used for object detection. And then, Hough Transformation is utilized for detecting the center of the pupil of an IRIS. In second phase, Daugmen’s Rubber Sheet model and Log Gabor filter is used for normalization and encoding and as a feature extraction method GNS (Global Neighborhood Structure) map is used, finally extracted feature of GNS is feed to the SVM (Support Vector Machine) for training and testing. For our tested dataset, experimental results demonstrate 92% accuracy in real portion and 86% accuracy in imaginary portion for both eyes. In addition, our proposed model outperforms than other two conventional methods exhibiting higher accuracy.
A UTOMATIC C OMPUTATION OF CDR U SING F UZZY C LUSTERING T ECHNIQUEScsandit
Eye disease identification techniques are highly im
portant in the field of ophthalmology. A
vertical Cup-to-Disc Ratio which is the ratio of th
e vertical diameter of the optic cup to that of
the optic disc, of the fundus eye image is one of t
he important signs of glaucoma. This paper
presents an automated method for the extraction of
optic disc and optic cup using Fuzzy C
Means clustering technique. The validity of this ne
w method has been tested on 454 colour
fundus images from
three different publicly available databases DRION,
DIARATDB0 and
DIARETDB1 and, images from an ophthalmologist. The
average success rate of optic disc and
optic cup segmentation is 94.26percentage. The scat
ter plot depicts high positive correlation
between clinical CDR and the CDR obtained using the
new method. The result of the system
seems to be promising and useful for clinical work.
Transform Domain Based Iris Recognition using EMD and FFTIOSRJVSP
Iris is one of the physiological trait which is used to identify the individuals. In this paper Transform Domain Based Iris Recognition using EMD and FFT is proposed. Circular Hough Transform is used in the Preprocessing stage to extract circular part of eye. The circular iris part is converted into rectangular rubber sheet model in Region of Interest (ROI).Empirical Mode Functions (EMF)’s are obtained by applying Empirical Mode Decomposition (EMD) on the Iris. FFT is also applied on ROI to extract the features. These features are added arithmetically to obtain final features. The features of the database are compared with test iris using Euclidian Distance(ED) to compute performance parameters. It is observed that the values of CRR and EER are better in the case of propsed algorithm compared to existing algorithms.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
A Comprehensive Approach for Multi Biometric Recognition Using Sclera Vein an...IJTET Journal
Sclera and finger print vein fusion is a new biometric approach for uniquely identifying humans. First, Sclera vein is identified and refined using image enhancement techniques. Then Y shape feature extraction algorithm is used to obtain Y shape pattern which are then fused with finger vein pattern. Second, Finger vein pattern is obtained using CCD camera by passing infrared light through the finger. The obtained image is then enhanced. A line shape feature extraction algorithm is used to get line patterns from enhanced finger vein image. Finally Sclera vein image pattern and Finger vein image pattern were combined to get the final fused image. The image thus obtained can be used to uniquely identify a person. The proposed multimodal system will produce accurate results as it combines two main traits of an individual. Therefore, it can be used in human identification and authentication systems.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
WAVELET PACKET BASED IRIS TEXTURE ANALYSIS FOR PERSON AUTHENTICATIONsipij
There is considerable rise in the research of iris recognition system over a period of time. Most of the
researchers has been focused on the development of new iris pre-processing and recognition algorithms for
good quail iris images. In this paper, iris recognition system using Haar wavelet packet is presented.
Wavelet Packet Transform (WPT ) which is extension of discrete wavelet transform has multi-resolution
approach. In this iris information is encoded based on energy of wavelet packets.. Our proposed work
significantly decreases the error rate in recognition of noisy images. A comparison of this work with nonorthogonal Gabor wavelets method is done. Computational complexity of our work is also less as
compared to Gabor wavelets method.
A Survey : Iris Based Recognition SystemsEditor IJMTER
The security is one of the important aspect of today's life. Iris recognization is one of the leading
research of security which is used to identify the individual person. Usually iris based biometric is more better
than other biometric in terms of accuracy, fast, stability, uniqueness. The iris recognition system works by
capturing and storing biometric information and then compare scanned copy of iris biometric with the stored iris
images in the database. There are several Iris Based Recognition Systems are developed so far. In this paper we
presented several iris techniques and create a base for our future roadmap.
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.
IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcscpconf
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.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
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.
This paper compares two different techniques of iris recognition and explains the steps of extracting iris from eye image palette formation and conditioning of palette for matching. The focus of the paper is in finding the suitable method for iris recognition on the basis of recognition time, recognition rate, false detection rate, conditioning time, algorithm complexity, bulk detection, database handling. In this paper comparison has been performed two methods feature based and phase based recognition.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
MULTI SCALE ICA BASED IRIS RECOGNITION USING BSIF AND HOG sipij
Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person
effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris
Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation
(HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and
bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with
iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is
obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database
image with test image final features to compute performance parameters. It is observed that the
performance of the proposed method is better compared to existing methods.
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Proposition of local automatic algorithm for landmark detection in 3D cephalo...journalBEEI
This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.
Reeb Graph for Automatic 3D CephalometryCSCJournals
The purpose of this study is to present a method of three-dimensional computed tomographic (3D-CT) cephalometrics and its use to study cranio/maxilla-facial malformations. We propose a system for automatic localization of cephalometric landmarks using reeb graphs. Volumetric images of a patient were reconstructed into 3D mesh. The proposed method is carried out in three steps: we begin by applying 3d mesh skull simplification, this mesh was reconstructed from a head volumetric medical image, and then we extract a reeb graph. Reeb graph mesh extraction represents a skeleton composed in a number of nodes and arcs. We are interested in the node position; we noted that some reeb nodes could be considered as cephalometric landmarks under specific conditions. The third step is to identify these nodes automatically by using elastic mesh registration using “thin plate” transformation and clustering. Preliminary results show a landmarks recognition rate of more than 90%, very close to the manually provided landmarks positions made by a medical stuff.
A New Approach of Iris Detection and RecognitionIJECEIAES
This paper proposes an IRIS recognition and detection model for measuring the e-security. This proposed model consists of the following blocks: segmentation and normalization, feature encoding and feature extraction, and classification. In first phase, histogram equalization and canny edge detection is used for object detection. And then, Hough Transformation is utilized for detecting the center of the pupil of an IRIS. In second phase, Daugmen’s Rubber Sheet model and Log Gabor filter is used for normalization and encoding and as a feature extraction method GNS (Global Neighborhood Structure) map is used, finally extracted feature of GNS is feed to the SVM (Support Vector Machine) for training and testing. For our tested dataset, experimental results demonstrate 92% accuracy in real portion and 86% accuracy in imaginary portion for both eyes. In addition, our proposed model outperforms than other two conventional methods exhibiting higher accuracy.
A UTOMATIC C OMPUTATION OF CDR U SING F UZZY C LUSTERING T ECHNIQUEScsandit
Eye disease identification techniques are highly im
portant in the field of ophthalmology. A
vertical Cup-to-Disc Ratio which is the ratio of th
e vertical diameter of the optic cup to that of
the optic disc, of the fundus eye image is one of t
he important signs of glaucoma. This paper
presents an automated method for the extraction of
optic disc and optic cup using Fuzzy C
Means clustering technique. The validity of this ne
w method has been tested on 454 colour
fundus images from
three different publicly available databases DRION,
DIARATDB0 and
DIARETDB1 and, images from an ophthalmologist. The
average success rate of optic disc and
optic cup segmentation is 94.26percentage. The scat
ter plot depicts high positive correlation
between clinical CDR and the CDR obtained using the
new method. The result of the system
seems to be promising and useful for clinical work.
Transform Domain Based Iris Recognition using EMD and FFTIOSRJVSP
Iris is one of the physiological trait which is used to identify the individuals. In this paper Transform Domain Based Iris Recognition using EMD and FFT is proposed. Circular Hough Transform is used in the Preprocessing stage to extract circular part of eye. The circular iris part is converted into rectangular rubber sheet model in Region of Interest (ROI).Empirical Mode Functions (EMF)’s are obtained by applying Empirical Mode Decomposition (EMD) on the Iris. FFT is also applied on ROI to extract the features. These features are added arithmetically to obtain final features. The features of the database are compared with test iris using Euclidian Distance(ED) to compute performance parameters. It is observed that the values of CRR and EER are better in the case of propsed algorithm compared to existing algorithms.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Brain Tumor Extraction from T1- Weighted MRI using Co-clustering and Level Se...CSCJournals
The aim of the paper is to propose effective technique for tumor extraction from T1-weighted magnetic resonance brain images with combination of co-clustering and level set methods. The co-clustering is the effective region based segmentation technique for the brain tumor extraction but have a drawback at the boundary of tumors. While, the level set without re-initialization which is good edge based segmentation technique but have some drawbacks in providing initial contour. Therefore, in this paper the region based co-clustering and edge-based level set method are combined through initially extracting tumor using co-clustering and then providing the initial contour to level set method, which help in cancelling the drawbacks of co-clustering and level set method. The data set of five patients, where one slice is selected from each data set is used to analyze the performance of the proposed method. The quality metrics analysis of the proposed method is proved much better as compared to level set without re-initialization method.
A Comprehensive Approach for Multi Biometric Recognition Using Sclera Vein an...IJTET Journal
Sclera and finger print vein fusion is a new biometric approach for uniquely identifying humans. First, Sclera vein is identified and refined using image enhancement techniques. Then Y shape feature extraction algorithm is used to obtain Y shape pattern which are then fused with finger vein pattern. Second, Finger vein pattern is obtained using CCD camera by passing infrared light through the finger. The obtained image is then enhanced. A line shape feature extraction algorithm is used to get line patterns from enhanced finger vein image. Finally Sclera vein image pattern and Finger vein image pattern were combined to get the final fused image. The image thus obtained can be used to uniquely identify a person. The proposed multimodal system will produce accurate results as it combines two main traits of an individual. Therefore, it can be used in human identification and authentication systems.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
WAVELET PACKET BASED IRIS TEXTURE ANALYSIS FOR PERSON AUTHENTICATIONsipij
There is considerable rise in the research of iris recognition system over a period of time. Most of the
researchers has been focused on the development of new iris pre-processing and recognition algorithms for
good quail iris images. In this paper, iris recognition system using Haar wavelet packet is presented.
Wavelet Packet Transform (WPT ) which is extension of discrete wavelet transform has multi-resolution
approach. In this iris information is encoded based on energy of wavelet packets.. Our proposed work
significantly decreases the error rate in recognition of noisy images. A comparison of this work with nonorthogonal Gabor wavelets method is done. Computational complexity of our work is also less as
compared to Gabor wavelets method.
A Survey : Iris Based Recognition SystemsEditor IJMTER
The security is one of the important aspect of today's life. Iris recognization is one of the leading
research of security which is used to identify the individual person. Usually iris based biometric is more better
than other biometric in terms of accuracy, fast, stability, uniqueness. The iris recognition system works by
capturing and storing biometric information and then compare scanned copy of iris biometric with the stored iris
images in the database. There are several Iris Based Recognition Systems are developed so far. In this paper we
presented several iris techniques and create a base for our future roadmap.
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.
IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcscpconf
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.
Iris recognition for personal identification using lamstar neural networkijcsit
One of the promising biometric recognition method is Iris recognition. This is because the iris texture provides many features such as freckles, coronas, stripes, furrows, crypts, etc. Those features are unique for different people and distinguishable. Such unique features in the anatomical structure of the iris make it
possible the differentiation among individuals. So during last year’s huge number of people have been
trying to improve its performance. In this article first different common steps for the Iris recognition system
is explained. Then a special type of neural network is used for recognition part. Experimental results show high accuracy can be obtained especially when the primary steps are done well.
Pupil Detection Based on Color Difference and Circular Hough Transform IJECEIAES
Human pupil eye detection is a significant stage in iris segmentation which is representing one of the most important steps in iris recognition. In this paper, we present a new method of highly accurate pupil detection. This method is consisting of many steps to detect the boundary of the pupil. First, the read eye image (R, G, B), then determine the work area which is consist of many steps to detect the boundary of the pupil. The determination of the work area contains many circles which are larger than pupil region. The work area is necessary to determine pupil region and neighborhood regions afterward the difference in color and intensity between pupil region and surrounding area is utilized, where the pupil region has color and intensity less than surrounding area. After the process of detecting pupil region many steps on the resulting image is applied in order to concentrate the pupil region and delete the others regions by using many methods such as dilation, erosion, canny filter, circle hough transforms to detect pupil region as well as apply optimization to choose the best circle that represents the pupil area. The proposed method is applied for images from palacky university, it achieves to 100 % accuracy
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%.
Recent developments in iris based biometric authentication systemseSAT Journals
Abstract Authenticating the identity of an individual has become one of the most important aspects in many applications today. The iris based biometric system has therefore gained huge attention in this field. This strong interest is driven by its high level of accuracy and highly promising applications. This paper presents a comprehensive study of the various researches carried out in this field and the several techniques which forms a part of the biometric system using iris based recognition. The main emphasis is laid on the techniques or methods for segmentation, feature extraction and matching. Various methods for all this issues have been discussed in order to examine the state of art. Finally some challenges and future scopes have been discussed. Keywords:-Iris based recognition, segmentation, feature extraction, matching insert.
Secure System based on Dynamic Features of IRIS Recognitionijsrd.com
Basically, the idea behind this system is improvement in cybernetics, the biometric person identification technique based on the pattern of the human iris is well suited to be applied to access control. The human eye is sensitive to visible light. Security systems having realized the value of biometrics for two basic purposes: to verify or identify users. In this busy world, identification should be fast and efficient. In this paper I focus on an efficient methodology for identification and verification for iris detection using Haar transform and Minimum hamming distance. I use canny operator for the edge detection. This biological phenomenon contracts and dilates the two pupils synchronously when illuminating one of the eyes by visible light .I applied the Haar wavelet compressing the data. By comparing the quantized vectors using the Hamming Distance operator, we determine finally whether two irises are similar. The result shows that system is quite effective.
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A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
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Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
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Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
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Bob Boule
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
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Cyber risk predictions
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
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1. Swati Pandey et al. / International Journal on Computer Science and Engineering (IJCSE)
Iris Pattern Segmentation using
Automatic Segmentation and Window
Technique
Swati Pandey1
Department of Electronics and Communication
University College of Engineering, Rajasthan Technical University, Kota
vyas.swati28@gmail.com
Prof. Rajeev Gupta
Department of Electronics and communication
University College of Engineering, Rajasthan Technical University, Kota,
rajeev_eck@yahoo.com
Abstract: A Biometric system is an automatic identification of an individual based on a unique feature or
characteristic. Iris recognition has great advantage such as variability, stability and security. In this
paper, use the two methods for iris segmentation -An automatic segmentation method and Window
method. Window method is a novel approach which comprises two steps first finds pupils' center and
then two radial coefficients because sometime pupil is not perfect circle. The second step extract the
information of the pupil center and find edges on a one-dimensional imaginary line to each side of the
pupil. Localization of iris image is required before iris matching. By experiments we conclude that
automatic segmentation algorithm gives 84% accuracy while window technique gives 99% accurate result
for Pupil boundary and 79 % for iris edge detections in presence of high degree of eyelashes and eyelids
covering the iris region.
Keywords-Circular Hough Transform; Iris localization; Normalization; rubber sheet mode; Iris basis
matrix.
I. INTRODUCTION
Biometric refers to the identification & verification of human identity based on certain physiological trait of
a person. The commonly used biometric feature includes speech fingerprint, face, handwriting, hand geometry
etc, in these methods iris recognition is regarded as a high accuracy verification technology, so that many
country have idea of adopting iris recognition to improve safety of their key departments. The iris is the
pigmented colored part of the eye behind the eyelids and in front of lens. It is the only internal organ of the body
which is normally externally visible. These visible patterns are unique to individuals and it has been found that
the probability of finding similar pattern is almost 'ZERO'. Iris recognition system includes certain steps i.e. iris
collection, preprocessing, segmentation, normalization, feature extraction and pattern matching. In this paper,
we are focused on segmentation of iris pattern.
Several researchers have introduced various methods for iris finding and localization. John Daugman [1] has
proposed very first practical and robust methodologies, providing the base work of many functioning systems.
He used integro differential operator to find both the iris inner and outer boundaries contour. Shamsi [4] also use
integro differential operator for iris segmentation. Wildes [7], Kong and Zhang [2] and Ma et al. [8] proposed a
circular Hough transform method with gradient-based binary edge map construction. Narote et. al [3] has
proposed modification circular Hough transform to determine an automated threshold for binarization based on
histogram. Bodade and talbar [5] presents a novel approach of accurate iris segmentation using two images
captured at two different intensities. Yahya and Nordin [6] & Fitzgibbon et al. [9], proposed a new method
based on Direct least squares fitting of ellipse to detect the inner boundary of iris. This method uses Ellipse
fitting instead of circle fitting of iris shape. The ellipse fitting is required for real iris images. This method
combines several advantages:
• It is ellipse-specific, so that even bad data will always return an ellipse.
• It can be solved naturally by a generalized eigen system.
• It is extremely robust, efficient, and easy to implement.
II. SEGMENTATION TECHNIQUES:
In this paper, we are discussed two approaches for iris segmentation. First, Hough transform based method is
used for iris localization and rubber sheet modal is for normalization. And second, a novel approach window
technique in which finds the center of the pupil and two radial coefficients due to elliptical shape of pupil. Then
ISSN : 0975-3397
Vol. 5 No. 01 Jan 2013
1
2. Swati Pandey et al. / International Journal on Computer Science and Engineering (IJCSE)
extract the information of the pupil center and tries to find edges on a one-dimensional imaginary line to each
side of the pupil.
A.
Image Acquisition
First step of iris recognition system is captured iris region of the human eye image. Eye iris is very small and
black object so it must be captured at a short distant about 4cm to 13 cm under a good illumination environment.
The objective iris image may have undesired portion of eyes like sclera, eyelid, pupil, the area of this is also a
dependent parameters on eye to camera distance & angle of camera. To improve the quality of iris images
preprocessing steps are required. In this paper, we have used CASIA database for experiments. Image
acquisition is the basic step of iris recognition system and after that iris segmentation is done.
III. AUTOMATIC SEGMENTATION TECHNIQUE
This method evolved the following stepsA.
Iris Localization
To detect the iris image, which is an annular portion between the pupil (inner boundary) and the sclera
(outer boundary) localization is required. Pupil is the black circular part surrounded by iris tissues. Outer radius
of the iris Pattern can be detected with the help of the center of the pupil. The process of localization requires
detection of the inner boundaries (limiting part between iris and pupil) & outer boundary (limiting edge of iris &
sclera)
Firstly convert the iris image into grey scale image to remove the illumination effect. In this paper circular
Hough transform is employed for detecting the iris and Pupil boundaries. Hough transform perform better when
image muddled with artifacts like shadows and noise. This transform first, find the intensity image gradient in
the given image at all the locations by convolving with the Sobel filters. Sobel filter are used 3*3 Kernels to
calculate the gradient (intensity variation) of image in vertical direction Gver and horizontal direction Ghor. The
Sobel filter kernels are:
Cver ={-1 -2 -1;0 0 0; 1 2 1}
(1)
Chor ={-1 0 1;-2 0 2; -1 0 1}
(2)
The absolute value of gradient images along horizontal and vertical direction is by following equation;
Gabs = Gver + Ghor
(3)
Where Gver is the convolution of the image with Cver and Ghor is the convolution of the image with Chor. The
edge map of absolute gradient is obtained using canny edge detection. Center of the edge image is determined
with the following equation:
xc = x - r*cos(θ)
yc = y - r*sin(θ)
(4)
(5)
Where x,y are the coordinates at pixel P and r is the possible range of radius values, θ(theta) ranges [0;π] .
A maximum point in the Hough space will correspond of the radius r and center coordinates xc & yc of the circle.
If using all gradient data, Hough transform is performs firstly for iris/ sclera boundary then apply for iris/pupil
boundary. After the completion, the radius, x-y center coordinates for both circles are obtained. The image
eyelid and eyelashes are integral part of captured image which affect the effective information of iris, to filter
out the undesired part (eyelid, eyelashes, sclera) we use the linear Hough transform. To isolate the eyelid first
fitting a line to the upper and lower eyelid with linear Hough transforms. A second horizontal line is drawn,
which intersects with the first line at the iris edge that is closest to the pupil. For horizontal gradient information
canny edge detector is used & only horizontal component information is taken.
B.
Normalization
Between eye images have dimensional inconsistencies due to stretching of the iris caused by pupil dilation
from varying level of illumination. Normalization process rectifies those inconsistencies which are produce due
to head tilt, imaging distance, rotation of the camera etc. Normalization process will produce iris images, which
have same constant dimensions for obtain the two different snap of one iris image under different conditions
should same. In normalization we convert the circular iris region into rectangular region. To normalize the iris
ISSN : 0975-3397
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3. Swati Pandey et al. / International Journal on Computer Science and Engineering (IJCSE)
region Daugman’s rubber sheet model is used. Here, we remap each point within the iris region to a pair of polar
coordinates(r,θ),where r is on the interval[0,1] and θ is angle[0,2π].
In normalization process, changed the coordinate system by unwrapping the iris and all the point within the
iris boundary are mapped into their polar equivalent. Pupil’s centre is taken as referral point, and radial vectors
pass through the iris region. A number of data points are selected along each radial line and this is defined as the
radial resolution. The number of radial lines going around the iris region is defined as the angular resolution.
Since the pupil can be non-concentric to the iris, a remapping formula is needed to rescale points depending on
the angle around the circle.
IV. WINDOW TECHNIQUE
A.
Pupillary boundary
In this paper, CASIA database eye image is used for the experiment. To find the pupil, we first need to apply
a linear threshold in the image, Where, I is the original image and g is the threshold image. If intensity of Pixels
greater than the empirical value of 70 (in a 0 to 256 scale) are dark pixels, therefore converted to 1 (black).
Pixels intensity is smaller than or equal to 70 are assigned to 0 (white). Next, we apply Freeman’s chain code to
find regions of 8-connected pixels that are assigned with value equal 1. There is also a possible to see that
eyelashes also satisfy the threshold condition, but its area is smaller than the pupil area. Using this knowledge,
we can cycle through all regions.
Finally, we apply the chain code algorithm in order to retrieve the only region in the image (i.e. the pupil).
From this region, it is trivial to obtain its central moments. Finding the edges of the pupil involves the creation
of two imaginary orthogonal lines passing through the centroid of the region. The binarized pupil boundaries are
defined by the first pixel with intensity zero; from the center to the extremities. It is very efficient and reliable
method.
B.
Iris edge detection
In this step of iris segmentation, finding the contour of the iris. The first problem comes from the anatomy
of the eye and the fact that every person is different. Sometimes the eyelid may occlude part of the iris and not
perfect circle may be assumed in this case. Other times due to variation in gaze direction the iris center will not
match the pupil center, and we will have to deal with strips of iris of different width around the pupil.
We are considering that areas of the iris at the right and left of the pupil are the ones that most often present
visible for data extraction. The areas above and below the pupil have unique information, but there is a
possibility that they are totally or partially occluded by eyelash or eyelid. A technique adopted for iris detection
which use the information from pupillary boundary section to trace a horizontal imaginary line that crosses the
whole image passing through the center of the pupil. We analyze that the signal composed by pixel intensity
from the center of the image towards the border and try to detect abrupt increases of intensity level. Although
the edge between the iris disk and the sclera are smooth and it has greater intensity than iris pixels. We intensify
this difference applying a linear contrast filter. There is possibility of sudden rise in intensity because some
pixels inside the iris disk are very bright. So, detection of iris edge at that point gets fail. To prevent that from
happening, we take the intensity average of small windows and then detect when the sudden rises occur from
these intervals.
The following steps taken to detect the edges of the iris image I(x,y).
•
With papillary boundary algorithm,
find the center (xcp,ycp) of the pupil and the pupil radius rx and then apply a linear contrast filter on
image
I(x,y): G(x, y) = I(x, y).α
•
Create vector V={v1,v2,…,vw} that holds pixel intensities of the imaginary row passing through the
center of the pupil (rx), with w being the width of contrasted image G(x,y).
•
Create vector R={rxcp+ rx r, rxcp+ rx + 1 ,…,rw} from the row that passes through the center of the pupil (ycp)
in contrasted iris image G(x,y). Vector R formed by the elements of the ycp line that start at the right
fringe of the pupil (xcp+rx) and go all the way to the width (w) of the image. Experiences shown that
adding a small margin to the fringe of the pupil provides good results as it covers for small errors of the
“findpupil” algorithm.
•
Similar as described above, create vector L={lxcp-rx,lxcp-rx-1,…,r1} from row (ycp) of G(x,y). This time we
are forming vector L which contains elements of pupil center line starting at the left fringe of the pupil
and ending at the first element of that line.
•
For each side of the pupil (vector R for the right side and vector L for the left side):
Calculate the average window vector A={a1,…,an} where n=|L| or n=|R|. Vector A is subdivided in
i windows of size ws. For all window i1 n/ws , elements a i.ws-ws…ai.ws will contain the average of that
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window. We found through experiments that a window size ws=15 provides satisfactory results for
the CASIA Iris database.
Identify the edge point given side of the iris (vector L or R) as the first increase of values in Aj
(1≤j≤n) that exceeds a set threshold t. In our experiments, a value of t equal to 10 has shown to
identify the correct location of the iris edge. The advantage of this algorithm is performance. For
an image of size m.n, the complexity is O(m.n) for the contrast operation and O(m) for edge
finding, total complexity of O(m.n). The reader must be warned though that algorithm efficiency
and reliability highly depends on carefully chosen threshold (t) and window size (ws). Other
modifications to the algorithm may also help improve the overall accuracy of the algorithm for
instance adding margins to the sides of the pupil.
Fast computation comes with a price, and the algorithm is very sensitive to local intensity variation (or lack of).
C.
Feature extraction
So far we have performed the segmentation of the iris in two steps to reduce the size of vector pattern and to
isolate only information that distinguishes individuals, namely the iris patterns. Reduced size of iris pattern is
required because the CASIA Iris Database provides images that are 320x280 i.e. 89,600 pixels. This dimension
is too high for today’s computing power, and even though we had such capacity, and it also not gives the
satisfactory results.
D.
Forming iris basis: overall Strategy
Iris Basis is our first attempt to reduce the dimension of the eye image and focused only one part of the eye that
effectively identify the desired part. Also, we are restricted to map only those areas of iris which has less
influence of eyelashes and eyelids. Assuming that intra-class rotation of iris is practically void, we propose an
approach, reduce the size of the iris image and extract pixels of either side of the pupil The overall strategy is as
follows: given image, desired number of Iris Basis rows and columns,
•
Retrieve pupil center, radius and iris endpoints.
•
Calculate height of pupil (2 times radius) and space between rows (s) as pupil height divided by desired
number of Iris Basis rows.
•
Calculate index of the first target row as center of pupil – vertical pupil radius, assuming that first row is
at the top of the image.
•
For each side of the pupil, find baseline width as iris edge of current side – pupil edge of current side.
And for all baselines, starting at the top of the pupil, ending at the bottom of the pupil and spaced by s,
perform the following steps Calculate, using the equation of the circle, the (x,y) location of pixel that resides in the intersection
of current baseline and circle centered at pupil center with radius equal to pupil horizontal radius .
Map pixels that are under the baseline to vector B with number of elements equal to half of
desired number of columns. Use an average mask of 3x3 pixels to calculate pixel intensity.
Append vector B to Iris Basis matrix of respective side of the iris
Merge the two halves of Iris Basis matrices side by side into one final Iris Basis matrix.
This is an attempt to avoid eyelashes and eyelids. The final result of classification will depend only on texture
features are in samples. This algorithm considers only those features to the sides of the pupil.
V. EXPERIMENTAL RESULT
The iris images in our experiment are from Chinese Academy of Science Institute of Automation database
(CASIA data base) [10]. The resolution of each image is 320x280 pixels, 256 grey scale. There are 108 datasets
from 108 users each dataset has 7 iris images.
First method uses the Circular Hough transform for iris localization. An automatic segmentation algorithm is
presented, which would localize the iris region from an eye image and isolate eyelid, eyelash and reflection area.
Then normalization is performed to eliminate the inconsistencies between iris regions. Figure (1-5) shows the
result for automatic segmentation technique.
Second method is window technique, which re-samples iris images into 40x40 pixels of 1601 pattern vector,
is used for segmentation. Later Technique involves two steps; first is pupil detection algorithm which gives 99
% accuracy but its iris detection algorithm gives 79 % accuracy in case of high degree of eyelash overlapping
and eyelids covering part of the iris or the sclera was not as white as expected. Figure (6-9) shows the
segmentation results for window technique.
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Fig.1. Eye- Iris Segmentation
Fig.2 Upper and lower eyelid detection
Fig 3. Normalised eye image
Fig 4. Polar noise model
Fig 5. Segmented iris
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Fig 6. Pupil finder(Find the pupil center)
Fig 7. Cycles through all images of the database and gives the final count of segmentation successes or failures.
Fig 8. Iris finders (perform image segmentation and finds the edge points of the iris at the horizontal line that crosses the center of the
pupil)
Fig 9. Iris basis construction & visualization
VI. CONCLUSION
In this paper, we have used two techniques for iris image segmentation; Automatic segmentation algorithm
and window technique. Automatic segmentation technique based on assumption that iris are always circular in
shape. Window technique extracts the areas of the iris at the right and left of the pupil are the ones that most
often visible for data extraction. The areas above and below the pupil have unique information, but there is a
possibility that they are totally or partially occluded by eyelash or eyelid. So, this method is unaffected by shape
of the iris. The proposed window technique involves two steps for matching i.e. pupil detection and iris
detection. Improvement in iris detection algorithm will provide the improved overall performance of the
window technique.
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