Effective Biometric system with using of iris detection.pptx
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This document summarizes an efficient biometric system using iris detection. It discusses how iris recognition is an effective biometric identification method as the iris texture remains stable over a person's lifetime. The proposed system involves detecting the pupil boundary using pupil boundary detection, localizing the flex and full light reflex, and contrast stretching. It then performs seed filling and detects the iris boundary, providing samples of detecting the iris boundary with the proposed system. In conclusion, iris recognition is highlighted as the most accurate biometric identification due to the stability of iris texture over a lifetime.
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Iris recognition system
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.
A Robust Approach in Iris Recognition for Person Authentication
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.
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Biometrics is science of measuring and statistically analyzing biological data. Biometric system establishes identity of a person based on unique physical or behavioural characteristic possessed by an individual.Behavioural biometrics measures characteristics which are acquired naturally over time. Physical biometrics measures inherent physical characteristics on a n individual. Over the last few decades enormous attention is drawn towards ocular biometrics. Cues provided by ocular region have led to exploration of newer traits. Feasibility of periocular region as a useful biometric trait has been explored recently. With the promising results of preliminary examination, research towards periocular region is currently gaining lot of prominence. Researchers have analyzed various techniques of feature extraction and classification in the periocular region. The current paper investigates the effect of using Lower Central Periocular Region (LCPR) for identification. The results obtained are comparable with those acquired for the entire periocular region with an advantage of reduced periocular area
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The document discusses multi-biometric identification using iris and periocular biometrics. It provides an overview of iris and periocular biometrics, including the structure of the eye, advantages of iris recognition, acquisition techniques, preprocessing, segmentation approaches, feature extraction methods, and current research directions. Some key points covered include the use of iris recognition in large-scale ID programs, the subsystems of ocular biometrics like iris, retina, cornea, techniques like hybrid biometrics that combine iris and other biometrics, and segmentation approaches such as using active contours, Hough transforms, graph cuts and cellular automata.
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Iris recognition is a method of biometric identification.
Biometric identification provides automatic recognition of an
individual based on the unique feature of physiological
characteristics or behavioral characteristic. Iris recognition is a
method of recognizing a person by analyzing the iris pattern.
This survey paper covers the different iris recognition techniques
and methods. IRJET-Unconstraint Eye Tracking on Mobile Smartphone
This document presents a study on developing an unconstrained eye tracking system using only the front camera of a mobile smartphone. It aims to create a low-cost eye tracking solution. The researchers designed techniques to detect faces, eyes and irises from camera images using Haar cascade classification and circular Hough transform. They tested the system under various conditions like lighting changes, wearing glasses, in dark environments, and while driving. The techniques were able to accurately detect eyes in different scenarios. The system has applications in areas like driving assistance systems and could be integrated into vehicles.
iris recognition system as means of unique identification
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
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This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, describing how it was first proposed in 1987 and patented by John Daugman in 1994. The document outlines the basic stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes some advantages like the uniqueness and stability of iris patterns. Finally, it provides an update on the current status of the project, which is currently in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.
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Iris recognition system
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.
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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.
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Iris recognition is a method of biometric identification.
Biometric identification provides automatic recognition of an
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This survey paper covers the different iris recognition techniques
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This document presents a study on developing an unconstrained eye tracking system using only the front camera of a mobile smartphone. It aims to create a low-cost eye tracking solution. The researchers designed techniques to detect faces, eyes and irises from camera images using Haar cascade classification and circular Hough transform. They tested the system under various conditions like lighting changes, wearing glasses, in dark environments, and while driving. The techniques were able to accurately detect eyes in different scenarios. The system has applications in areas like driving assistance systems and could be integrated into vehicles.
iris recognition system as means of unique identification
Project Done and submitted by Students Of final year CBP Government Engineering College
student name : vipin kumar khutail , Krishnanad Mishra , Jaswant kumar, Rahul Vashisht
Project Description :
Iris recognition is an automated method of bio-metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex random patterns are unique, stable, and can be seen from some distance
12
This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, describing how it was first proposed in 1987 and patented by John Daugman in 1994. The document outlines the basic stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes some advantages like the uniqueness and stability of iris patterns. Finally, it provides an update on the current status of the project, which is currently in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.
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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.
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This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, pioneered by Flom and Safir in 1987 and later patented by John Daugman. The document outlines the structure of the eye and iris, and describes the typical stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes advantages like the uniqueness and stability of iris patterns. Finally, it states the current status of the project, which is in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.
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This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, pioneered by Flom and Safir in 1987 and later patented by John Daugman. The document outlines the structure of the eye and iris, and describes the typical stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes advantages like the uniqueness and stability of iris patterns. Finally, it states the current status of the project, which is in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...
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
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An exploration of periocular region with reduced region for authentication re...
Biometrics is science of measuring and statistically analyzing biological data. Biometric system
establishes identity of a person based on unique physical or behavioral characteristic possessed
by an individual. Behavioral biometrics measures characteristics which are acquired naturally
over time. Physical biometrics measures inherent physical characteristics on an individual.
Over the last few decades enormous attention is drawn towards ocular biometrics. Cues
provided by ocular region have led to exploration of newer traits. Feasibility of periocular
region as a useful biometric trait has been explored recently. With the promising results of
preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and
classification in the periocular region. This paper investigates the effect of using Lower Central
Periocular Region (LCPR) for identification. The results obtained are comparable with those
acquired for full periocular texture features with an advantage of reduced periocular area.
AN EXPLORATION OF PERIOCULAR REGION WITH REDUCED REGION FOR AUTHENTICATION : ...
Biometrics is science of measuring and statistically analyzing biological data. Biometric system establishes identity of a person based on unique physical or behavioral characteristic possessed by an individual. Behavioral biometrics measures characteristics which are acquired naturally over time. Physical biometrics measures inherent physical characteristics on an individual. Over the last few decades enormous attention is drawn towards ocular biometrics. Cues provided by ocular region have led to exploration of newer traits. Feasibility of periocular region as a useful biometric trait has been explored recently. With the promising results of preliminary examination, research towards periocular region is currently gaining lot of
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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%.
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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.
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This document provides an overview of iris recognition technology. It discusses the motivation for using biometric authentication like iris recognition. It then reviews the history of iris recognition, pioneered by Flom and Safir in 1987 and later patented by John Daugman. The document outlines the structure of the eye and iris, and describes the typical stages of iris recognition including image acquisition, iris localization, normalization, feature extraction, and pattern matching. It notes advantages like the uniqueness and stability of iris patterns. Finally, it states the current status of the project, which is in the iris localization phase using an eye image dataset from Chinese University of Hong Kong.
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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,
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wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on
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the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and
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Biometrics is science of measuring and statistically analyzing biological data. Biometric system
establishes identity of a person based on unique physical or behavioral characteristic possessed
by an individual. Behavioral biometrics measures characteristics which are acquired naturally
over time. Physical biometrics measures inherent physical characteristics on an individual.
Over the last few decades enormous attention is drawn towards ocular biometrics. Cues
provided by ocular region have led to exploration of newer traits. Feasibility of periocular
region as a useful biometric trait has been explored recently. With the promising results of
preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and
classification in the periocular region. This paper investigates the effect of using Lower Central
Periocular Region (LCPR) for identification. The results obtained are comparable with those
acquired for full periocular texture features with an advantage of reduced periocular area.
AN EXPLORATION OF PERIOCULAR REGION WITH REDUCED REGION FOR AUTHENTICATION : ...
Biometrics is science of measuring and statistically analyzing biological data. Biometric system establishes identity of a person based on unique physical or behavioral characteristic possessed by an individual. Behavioral biometrics measures characteristics which are acquired naturally over time. Physical biometrics measures inherent physical characteristics on an individual. Over the last few decades enormous attention is drawn towards ocular biometrics. Cues provided by ocular region have led to exploration of newer traits. Feasibility of periocular region as a useful biometric trait has been explored recently. With the promising results of preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and classification in the periocular region. This paper investigates the effect of using Lower Central
Periocular Region (LCPR) for identification. The results obtained are comparable with those acquired for full periocular texture features with an advantage of reduced periocular area
EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...
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%.
Spectacle Design Preferences in Oman Society
The document discusses a study conducted in Oman to investigate preferences for spectacle frame designs. A questionnaire was distributed to 100 participants aged 15 and older to evaluate their choices. Key findings included:
- The most popular design chosen was square frames (63%), followed by browline, rimless, and others. The least preferred was half-eye frames.
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The study provides
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This document summarizes a research paper that proposes integrating head pose information with a 3D multi-texture active appearance model (MT-AAM) to improve gaze detection from webcam images. The 3D MT-AAM combines an iris texture model with an eye skin model that has a hole for the iris region. This allows the iris texture to rotate realistically under the skin for different gaze directions. The paper also proposes a multi-objective optimization that applies the 3D MT-AAM to both eyes, weighting the results based on detected head pose to determine which eye is more visible. Experimental results showed this approach outperformed a standard AAM and was comparable to state-of-the-art methods that require manual initialization.
COMPATIBILITY OF PROGRESSIVE GLASSES IN RELATION TO AGE, REFRACTIVE ERROR AND...
COMPATIBILITY OF PROGRESSIVE GLASSES IN RELATION TO AGE, REFRACTIVE ERROR AND...Shree Bharatimaiya College of Optometry & Physiotherapy
COMPATIBILITY OF PROGRESSIVE GLASSES IN RELATION TO AGE, REFRACTIVE ERROR AND OCCUPATION OF PATIENT:
Journal: Sabargam International Journal of Research in Multidiscipline
ISSN: 2456-4672 Volume I, Issue II, Jan 2017A010320106
This document presents a new approach for human identification using sclera recognition. It begins with background on sclera and challenges with sclera recognition. It then describes the proposed methodology which includes sclera segmentation, feature extraction using Gabor filtering, and recognition using Bayesian classification. Experimental results show the false accept and reject rates for the approach. It concludes that sclera recognition is promising for human identification and can achieve accuracy comparable to iris recognition in visible light. The proposed approach uses Bayesian classification for recognition, which is more effective than previous matching score methods.
L_3011_62.+1908
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.
BIOINSPIRATIONS FOR OPTICS, SENSORS AND PHOTONICS
The document provides information about a doctoral seminar on bioinspiration for optics, photonics, and sensorics. It discusses how nature inspires technology in these fields. Insects are a common source of bioinspiration due to their advanced visual systems. The document outlines several examples of innovations inspired by insect optics, including a zoom camera inspired by the compound eye, an artificial compound eye lens inspired by mosquitoes, and a multispectral sensor inspired by the eyes of Morpho butterflies. It also discusses bioinspired innovations in photonics, such as improved LEDs inspired by fireflies, and applications of bioinspiration across multiple disciplines.
Iris recognition
It is used to easily find the identification of the individual person.
This technique is more secure when compare to the all biometric readers.
Contact lenses
Effect of the pigment-free optical zone diameter of decorative tinted soft contact lenses on visual function.
The Inbuilt Password Iris
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.
Vol 5 No 1 - September 2013
Implementation of Ear Biometrics as Emerging Technology in Human Identification System ...............1
B. Srinivasan and V. K. Narendira Kumar
Determining the Security Enhancement of Biometrics in Internet Passport Scheme using Cryptographic
Algorithms ........................................................................................................................................1
B. Srinivasan and V. K. Narendira Kumar
Novel Image Fusion Techniques using DCT .........................................................................................1
V. P. S. Naidu
High Performance Data mining by Genetic Neural Network ................................................................1
Dadmehr Rahbari
Parallel Ensemble Techniques for Data Mining Application .................................................................1
M. Govindarajan
A Hybrid Cryptosystem for Image using Chaotic Mapping ...................................................................1
Nidhi Sethi and Sandip Vijay
Investigating Factors Affecting Adoption and Implementation of m-Government in the South African
Department of Home Affairs: An on-going Research ..........................................................................1
Maleshoane Sepeame and Emmanuel Babatunde Ajala
Prospects of Thermal Management Techniques in Microprocessor Architecture .................................1
Ajaegbu Chigozirim, Shodiya A.S and Kuyoro Shade O.
Capacity Based Clustering Model for Dense Wireless Sensor Networks ...............................................1
S. R. Boselin Prabhu and S. Sophia
Autorefractometer
An autorefractor or automated refractor is a computer-controlled machine used during an eye examination to provide an objective measurement of a person's refractive error and prescription for glasses or contact lenses. This is achieved by measuring how light is changed as it enters a person's eye. Autorefractometer is a Instruement which is using in optometrist and Opthalmologist clinic for objective refraction.
Now a days autorefractometer is very usefull Instruement for the refraction than retinoscope
NEURAL NETWORK APPROACH FOR EYE DETECTION
Driving support systems, such as car navigation systems are becoming common and they
support driver in several aspects. Non-intrusive method of detecting Fatigue and drowsiness
based on eye-blink count and eye directed instruction controlhelps the driver to prevent from
collision caused by drowsy driving. Eye detection and tracking under various conditions such as
illumination, background, face alignment and facial expression makes the problem
complex.Neural Network based algorithm is proposed in this paper to detect the eyes efficiently.
In the proposed algorithm, first the neural Network is trained to reject the non-eye regionbased
on images with features of eyes and the images with features of non-eye using Gabor filter and Support Vector Machines to reduce the dimension and classify efficiently. In the algorithm, first the face is segmented using L*a*btransform color space, then eyes are detected using HSV and Neural Network approach. The algorithm is tested on nearly 100 images of different persons under different conditions and the results are satisfactory with success rate of 98%.The Neural Network is trained with 50 non-eye images and 50 eye images with different angles using Gabor filter. This paper is a part of research work on “Development of Non-Intrusive system for realtime Monitoring and Prediction of Driver Fatigue and drowsiness” project sponsored by Department of Science & Technology, Govt. of India, New Delhi at Vignan Institute of Technology and Sciences, Vignan Hills, Hyderabad.
EYE TRACKING TECHNOLOGY
Eye tracking technology measures eye movements and gaze using cameras and light sources. It has various applications in research and product design. Eye trackers generally use infrared light and cameras to track the reflection of light sources off the eye to determine where a person is looking. Researchers can use eye tracking data to observe what stimuli capture people's attention and where their visual focus lies. The technology provides an unobtrusive way to understand how people interact with and process their environments.
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EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...
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An exploration of periocular region with reduced region for authentication re...
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EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...
INTEGRATING HEAD POSE TO A 3D MULTITEXTURE APPROACH FOR GAZE DETECTION
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◇回国时间很长，忘记办理；
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3:国家专业人才认证中心颁发入库证书
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
SCALING OF MOS CIRCUITS m .pptx
this ppt explains about scaling parameters of the mosfet it is basically vlsi subject
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Effective Biometric system with using of iris detection.pptx
- 1. An Efficient Biometric System Using Iris Detection Project Guide: Project Members: Mr.R.V.Ch.Shekar Rao P.Priyanka (Associate Professor) P.Gayathri V.Sai Moulika
- 2. INTRODUCTION • A biometric identification system of an individual is based on a unique biological feature of the individual features like human speech, fingerprints , iris, retina etc.. • Iris of the eye is considered as more effective. • Texture of the iris for an individual remains stable through life and can be encoded in a small memory. • This features makes recognition based iris the most accurate and reliable biometric identification.
- 3. HISTORY • The most well known methodologies are that proposed by Daugman in 1993 he presents an approach which was constituted the basis of most successful functioning iris recognition systems. • On the segmentation stage he uses a differential operator to find both the inner and outer iris boundary. • Hung,. Luo , and Chen in 2002 investigated the implementation of iris localization on downscale eye image to reduce search space. • In 2006, Arvacheh and Tizhoosh proposed active contour model to enhance pupil boundary detection.
- 4. HISTORY • Jarjes in 2010 identified the pupil boundary by circle fitting. the angular integral projection function has been used to detect points. • T. Muhammad, Tariq, Shahid, Aurangzeb and ling in 2012 investigated the implementation of iris localization based on a local histogram and standard deviation. • The pupil region is detected by of enclosing the pupil by using a circular moving window. • In 2013, Sastry and Sri introduced an enhanced iris segmentation method that allows iris recognition systems to be implemented in real-time applications. reduced iris segmentation time further allows high resolution iris images to be used thereby enhancing recognition accuracy. • Iman A.Saad and Loay E.George in 2014 introduced a method for iris segmentation. This method utilized histogram stretching as a preprocessing step to improve the contrast of the eye region.
- 6. PROPOSED SYSTEM Main Stages for Localisation of Iris are: • Detecting of Pupil Boundary Pupil Boundary Detection Localise Flexand Full the Light Reflex Seed Fill • Detecting of Iris Boundary Detecting of Iris Boundary
- 7. DETECTING OF PUPIL BOUNDARY
- 12. LOCLAEIZ FLEXAND FULL THE LIGHT REFLEX
- 15. SEED FILL
- 16. DETECTING OF IRIS BOUNDARY
- 17. DETECTING OF IRIS BOUNDARY
- 18. SAMPLES OF DETECTING IRIS BOUNDARY OF PROPOSED SYSTEM
- 19. CONCLUSION • Texture of the iris for an individual remains stable through life and can be encoded in a small memory. • This features make recognition based iris the most accurate and reliable biometric identification.