Week12
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Fingerprint scanners work by collecting a fingerprint sample and comparing it to samples on record. Fingerprints are formed from ridges that have unique patterns due to genetic and environmental factors. Most fingerprint scanners analyze and compare specific features called minutiae, such as ridge endings and bifurcations, between the scanned print and prints on file to identify a match.
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Biometrics clear presentation by Adduri kalyan(AK)
Biometrics refers to the automatic identification of individuals based on physiological or behavioral characteristics. It has been used in various forms throughout history, from fingerprinting in ancient China to modern uses like iris scanning. Biometric technologies provide important security benefits by being difficult to forge compared to tokens that can be stolen or forgotten. Physical biometrics include fingerprints, facial recognition, hand geometry, iris scans, and retina scans, while behavioral biometrics examine speaker recognition, signatures, and keystroke patterns. Biometrics systems work by collecting samples, extracting features to create templates, and making matches against stored templates to identify individuals. While providing strong identification, biometrics also have disadvantages like cost and potential user discomfort with some methods. Future prospects include emerging
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Fingerprint scanners work by collecting an image of a fingerprint and comparing its ridge and valley patterns to those stored in a database. Optical scanners use cameras while capacitive scanners detect ridge and valley patterns electrically. Scanners analyze fingerprints by measuring the relative positions of minutiae points like bifurcations. While convenient, fingerprint scanners can be fooled by fakes or identify non-matches, so combining them with passwords provides more secure authentication.
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Network security is enhanced through biometrics authentication which uses unique physical traits to verify user identity. Biometrics is more secure than passwords since traits cannot be forgotten, stolen, or easily copied. The document discusses common biometric traits like fingerprints, iris scans, and voice recognition. It explains how biometric systems work by enrolling traits during initial use then comparing submitted traits to stored information for authentication. Biometrics provides stronger security for networks and systems by using the human body as a verification method.
Finger Print Sensor
This seminar presentation summarizes fingerprint scanners. It discusses the history of fingerprint identification dating back to the 17th century. It then describes how fingerprint scanners work in mobile phones, including the general structure and technologies used like optical and capacitive scanners. Applications of fingerprint identification are also summarized, such as banking security, access control, and national ID systems. Both advantages like accuracy and disadvantages like potential errors are highlighted.
MDD Project Report By Dharmendra singh [Srm University] Ncr Delhi
In this modern era, a huge revolution in technology is the introduction of biometric recognition system. One of the most useful biometric recognition system is fingerprint recognition system. The fingerprint recognition system is considered to most important biometric system in addition to other biometrics recognition systems
Poster on biometrics
1. The document discusses how biometrics can enhance network security by providing unique authentication through physical traits like fingerprints, iris scans, and voice patterns.
2. Biometric systems work by enrolling users through capturing traits, storing trait data, and comparing new trait inputs to what is on file for authentication.
3. Common biometric technologies discussed are fingerprints, iris scanning, handwriting analysis, voiceprints, vein patterns, which can all uniquely identify individuals for security purposes. The document argues that biometrics provide more secure authentication than passwords.
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This document discusses biometrics authentication using physical and behavioral traits for security. Biometrics can identify users by their fingerprints, iris scans, facial recognition, voice patterns, and other characteristics. Popular biometric systems include fingerprint scanners, iris scans, and facial recognition. Biometric templates, which translate characteristics into codes, range in size from 9 bytes for hand geometry to 84 bytes to 2k for facial recognition. The document explores how biometric authentication works and is becoming more common for both commercial and personal use.
Recommended
Biometrics clear presentation by Adduri kalyan(AK)
Biometrics refers to the automatic identification of individuals based on physiological or behavioral characteristics. It has been used in various forms throughout history, from fingerprinting in ancient China to modern uses like iris scanning. Biometric technologies provide important security benefits by being difficult to forge compared to tokens that can be stolen or forgotten. Physical biometrics include fingerprints, facial recognition, hand geometry, iris scans, and retina scans, while behavioral biometrics examine speaker recognition, signatures, and keystroke patterns. Biometrics systems work by collecting samples, extracting features to create templates, and making matches against stored templates to identify individuals. While providing strong identification, biometrics also have disadvantages like cost and potential user discomfort with some methods. Future prospects include emerging
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Fingerprint scanners work by collecting an image of a fingerprint and comparing its ridge and valley patterns to those stored in a database. Optical scanners use cameras while capacitive scanners detect ridge and valley patterns electrically. Scanners analyze fingerprints by measuring the relative positions of minutiae points like bifurcations. While convenient, fingerprint scanners can be fooled by fakes or identify non-matches, so combining them with passwords provides more secure authentication.
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Network security is enhanced through biometrics authentication which uses unique physical traits to verify user identity. Biometrics is more secure than passwords since traits cannot be forgotten, stolen, or easily copied. The document discusses common biometric traits like fingerprints, iris scans, and voice recognition. It explains how biometric systems work by enrolling traits during initial use then comparing submitted traits to stored information for authentication. Biometrics provides stronger security for networks and systems by using the human body as a verification method.
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This seminar presentation summarizes fingerprint scanners. It discusses the history of fingerprint identification dating back to the 17th century. It then describes how fingerprint scanners work in mobile phones, including the general structure and technologies used like optical and capacitive scanners. Applications of fingerprint identification are also summarized, such as banking security, access control, and national ID systems. Both advantages like accuracy and disadvantages like potential errors are highlighted.
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In this modern era, a huge revolution in technology is the introduction of biometric recognition system. One of the most useful biometric recognition system is fingerprint recognition system. The fingerprint recognition system is considered to most important biometric system in addition to other biometrics recognition systems
Poster on biometrics
1. The document discusses how biometrics can enhance network security by providing unique authentication through physical traits like fingerprints, iris scans, and voice patterns.
2. Biometric systems work by enrolling users through capturing traits, storing trait data, and comparing new trait inputs to what is on file for authentication.
3. Common biometric technologies discussed are fingerprints, iris scanning, handwriting analysis, voiceprints, vein patterns, which can all uniquely identify individuals for security purposes. The document argues that biometrics provide more secure authentication than passwords.
Biometrics authentication
This document discusses biometrics authentication using physical and behavioral traits for security. Biometrics can identify users by their fingerprints, iris scans, facial recognition, voice patterns, and other characteristics. Popular biometric systems include fingerprint scanners, iris scans, and facial recognition. Biometric templates, which translate characteristics into codes, range in size from 9 bytes for hand geometry to 84 bytes to 2k for facial recognition. The document explores how biometric authentication works and is becoming more common for both commercial and personal use.
Week12
Fingerprint scanners work by optically or capacitively scanning fingerprints and analyzing unique ridge and valley patterns called minutiae. Optical scanners use charge-coupled device sensors like digital cameras to capture fingerprint images, while capacitive scanners detect ridge and valley differences electrically. Both create images of the fingerprint pattern, which scanner software then analyzes by measuring relative minutiae positions and shapes, similar to recognizing constellations, to determine if it matches fingerprints on file.
Week12
Fingerprint scanners work by optically or capacitively scanning fingerprints and analyzing unique ridge and valley patterns called minutiae. Optical scanners use charge-coupled device sensors like digital cameras to capture fingerprint images, while capacitive scanners detect ridge and valley differences electrically. Both create images of the fingerprint pattern, which scanner software then analyzes by measuring relative minutiae positions and shapes, similar to recognizing constellations, to determine if it matches fingerprints on record.
kiran's slide info
Biometrics uses physiological or behavioral characteristics to identify individuals. There are two main types: physiological (e.g. fingerprints, facial recognition) and behavioral (e.g. signature, voice). Biometric systems can operate in verification or identification mode. Verification compares a captured biometric to one on file to verify an individual's identity. Identification performs one-to-many comparisons to determine who an individual is. Common biometric technologies analyzed in the document include fingerprints, facial recognition, hand geometry, retinal scans, voice recognition, and signature analysis. Each method has advantages and disadvantages for security, accuracy, and usability. Biometrics is an evolving field that aims to increase identity security and convenience through technologies like iris scanning
Sw week12-b
Fingerprint scanners work by capturing the ridge pattern of fingerprints using either optical or capacitive scanning. Optical scanners use cameras to image fingerprints while capacitive scanners detect ridges using capacitive sensors. Both result in an image of the ridges and valleys. Analysis then focuses on distinctive minutiae points such as ridge endings and bifurcations to match fingerprints rather than the overall pattern.
Sw week12-b
Fingerprint scanners work in two main ways - optical scanners capture an image of ridges and valleys using light, while capacitive scanners detect ridges and valleys through electric fields. Both methods capture fingerprint features like ridge endings and bifurcations, which fingerprint analysis systems then use to identify matching fingerprints in a database rather than comparing entire fingerprint images. Fingerprint patterns are unique due to ridges developing as an evolutionary advantage for gripping.
Sw week12-b
Fingerprint scanners work in two main ways - optical scanners capture an image of ridges and valleys using light, while capacitive scanners detect ridges and valleys through electric fields. Both capture the unique pattern to identify individuals. Fingerprint analysis focuses on distinctive minutiae points like ridge endings and bifurcations rather than the overall print, as specific features provide a more accurate match than the complete pattern.
Fingerprint
Fingerprint identification is commonly used for biometric identification. Fingerprints are analyzed by their patterns (loops, whorls, arches) and ridge characteristics (minutiae). Most automatic fingerprint identification systems (AFIS) match minutiae features. The document proposes using soft computing techniques like neural networks and fuzzy logic to improve an AFIS. It describes a 5-layer network that uses minutiae extraction from divided fingerprint blocks to classify fingerprints hierarchically and reduce errors. Potential applications include security access, mobile unlock systems, driver's licenses, and national ID programs.
Fingerprint Based Biometric ATM Authentication System
Fingerprint Based Biometric ATM Authentication SystemInternational Journal of Engineering Inventions www.ijeijournal.com
This document summarizes a research paper on implementing a fingerprint-based biometric authentication system for ATMs using a PIC microcontroller. It describes how fingerprint identification works by analyzing ridge and valley patterns. The system uses a PIC16F877A microcontroller to collect fingerprint data from a fingerprint sensor module and match it to an enrolled fingerprint template to authenticate users. If a match is found, the ATM cashbox opens, and if not, an alarm sounds. The document discusses the methodology, advantages, limitations and components of the system, including the fingerprint sensor, microcontroller, LCD display, motor driver, and buzzer.How Fingerprint Scanner Work
Fingerprint scanners provide biometric security by verifying a user's identity based on their unique fingerprint ridges and valleys. Optical scanners use light reflection to generate an image of ridges and valleys, while capacitive scanners use electric fields. Both generate images of the fingerprint pattern. The scanner then analyzes and compares specific fingerprint features called minutiae points, like ridge endings and bifurcations, between the scanned fingerprint and stored fingerprints to identify a match. By matching a sufficient number of common minutiae points, the scanner can determine if two fingerprints belong to the same individual for authentication purposes.
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GANNON UNIVERSITYELECTR.docx
GANNON UNIVERSITY
ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT
FALL2015
GECE 572: DIGITAL SIGNAL PROCESSING
FINGER PRINT RECOGNITION USING MINUTIAE BASED FEATURE
FINAL PROJECT
Prepared by
THADASINA PRUTHVIN REDDY
[email protected]
SALMAN SIDDIQUI
[email protected]
Instructor:
Dr. Ram Sundaram
Table of contents
1. Abstract
2. Introduction
3. Fingerprint matching
4. Pre-processing stage
5. Minutiae extraction stage
6. Post-processing stage
7. Merits & Demerits
8. Applications & future scope
9. Conclusions
10.References
1. Abstract
Nowadays, conventional identification methods such as driver's license, passport, ATM cards and PIN codes do not meet the demands of this wide scale connectivity. Automated biometrics in general, and automated fingerprint authentication in particular, provide efficient solutions to these modern identification problems. Fingerprints have been used for many centuries as a means of identifying people. The fingerprints of individual are unique and are stay unchanged during the life time. Fingerprint matching techniques can be placed into two categories, minutiae-based and correlation based. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality the correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.
2. Introduction
Biometric recognition refers to the use of distinctive physiological (e.g. fingerprint, palm print, iris, face) and behavioral (e.g. gait, signature) characteristics, called biometric identifiers for recognizing individuals.
Fingerprint recognition is one of the oldest and most reliable biometric used for personal identification. Fingerprint recognition has been used for over 100 years now and has come a long way from tedious manual fingerprint matching. The ancient procedure of matching fingerprints manually was extremely cumbersome and time-consuming and required skilled personnel.
Finger skin is made up of friction ridges and sweat pores all along these ridges. Friction ridges are created during fetal life and only the general shape is genetically defined. The distinguishing nature of physical characteristics of a person is due to both the inherent individual genetic diversity within the human population as well as the random processes affecting the development of the embryo. Friction ridges ...
Fingerprint recognition (term paper) Project
This document discusses fingerprint recognition. It begins by defining fingerprint recognition as the automated process of verifying a match between two fingerprints. Fingerprints are a form of biometrics used to identify individuals due to their uniqueness. The document then discusses how fingerprints are distinguished by features called minutiae, specifically ridge endings and bifurcations. It also outlines some common fingerprint matching techniques such as correlation-based and minutiae-based matching.
Biometric and cyber security 1
- Biometrics such as fingerprints, iris scans, and facial recognition are increasingly being used for security and identification purposes in applications like access control, e-commerce, banking, and computer login to enhance security. Fingerprint recognition is a mature biometric technique used in this research.
- However, biometrics also present cybersecurity risks if the biometric data is stolen or systems are circumvented. Hackers could potentially force individuals to provide biometric samples or use facsimiles to gain unauthorized access to systems. Proper management of biometric data and systems is needed to mitigate risks.
L026070074
The document is a research paper that studies using a neural network model for fingerprint recognition. It discusses how fingerprint recognition is an important technique for security and restricting intruders. The paper proposes using an artificial neural network with backpropagation training to recognize fingerprints. It describes collecting fingerprint images, classifying them, enhancing the images, and training the neural network to match images and recognize fingerprints with high accuracy. The methodology, implementation, and results of using a backpropagation neural network for fingerprint recognition are analyzed.
Fingerprint recognition using minutiae based feature
Fingerprint recognition techniques can be categorized as minutiae-based or correlation-based. Minutiae-based techniques extract and match minutiae points like ridge endings and bifurcations. This approach has difficulties with low quality fingerprints. Correlation-based techniques match ridge patterns but require precise image registration. Fingerprint matching algorithms first enhance images, extract minutiae points, and compare points between images to determine a match.
Finger print authentication for bikes
The document discusses fingerprint-based authentication for embedded systems security. It begins with an introduction describing how embedded systems are increasingly networked and vulnerable. It then discusses fingerprint identification and authentication techniques. The rest of the document describes the hardware and software requirements for a fingerprint authentication system, including a microcontroller, fingerprint module, LCD, power supply, and other components. Block diagrams and working principles are provided to explain how the system functions to authenticate users via fingerprint and control access.
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Biometric in the field of Computer science ! This is a powerpoint presentation prepared as a first year participation in college presentation competition. Topic - Applications of biometrics in technology. This was my first attempt. Hope it comes in use for people in need of a simple presentation.
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This document discusses various biometric technologies including fingerprint recognition, iris scanning, retina scanning, voice recognition, signature verification, face recognition, and hand geometry recognition. It describes how each type of biometric works, including capturing biometric data, extracting distinguishing features, enrollment, verification, and matching against stored templates. Biometrics are increasingly used for identification and access control because they cannot be lost, stolen, or forgotten like ID cards or passwords. However, biometric systems must also account for changes in biometrics over time.
Roy
Fingerprint scanners work by capturing an image of a fingerprint using either optical or capacitance sensors. The image is then analyzed and compared to stored fingerprints to authenticate identity. While fingerprint scanners provide convenient authentication, a major downside is that stolen fingerprint data cannot be replaced like passwords, posing significant risks if identity is compromised.
Sw2assignment
Fingerprint scanners have become more common for security and access. They work by capturing an image of a fingerprint's unique ridge and valley patterns using either optical or capacitive sensors. The scanner then analyzes and compares these minutiae points to fingerprints stored in its database to authenticate identity. While fingerprint authentication has advantages over traditional passwords, there are also privacy concerns since fingerprints cannot be changed if compromised.
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Week12
Fingerprint scanners work by optically or capacitively scanning fingerprints and analyzing unique ridge and valley patterns called minutiae. Optical scanners use charge-coupled device sensors like digital cameras to capture fingerprint images, while capacitive scanners detect ridge and valley differences electrically. Both create images of the fingerprint pattern, which scanner software then analyzes by measuring relative minutiae positions and shapes, similar to recognizing constellations, to determine if it matches fingerprints on file.
Week12
Fingerprint scanners work by optically or capacitively scanning fingerprints and analyzing unique ridge and valley patterns called minutiae. Optical scanners use charge-coupled device sensors like digital cameras to capture fingerprint images, while capacitive scanners detect ridge and valley differences electrically. Both create images of the fingerprint pattern, which scanner software then analyzes by measuring relative minutiae positions and shapes, similar to recognizing constellations, to determine if it matches fingerprints on record.
kiran's slide info
Biometrics uses physiological or behavioral characteristics to identify individuals. There are two main types: physiological (e.g. fingerprints, facial recognition) and behavioral (e.g. signature, voice). Biometric systems can operate in verification or identification mode. Verification compares a captured biometric to one on file to verify an individual's identity. Identification performs one-to-many comparisons to determine who an individual is. Common biometric technologies analyzed in the document include fingerprints, facial recognition, hand geometry, retinal scans, voice recognition, and signature analysis. Each method has advantages and disadvantages for security, accuracy, and usability. Biometrics is an evolving field that aims to increase identity security and convenience through technologies like iris scanning
Sw week12-b
Fingerprint scanners work by capturing the ridge pattern of fingerprints using either optical or capacitive scanning. Optical scanners use cameras to image fingerprints while capacitive scanners detect ridges using capacitive sensors. Both result in an image of the ridges and valleys. Analysis then focuses on distinctive minutiae points such as ridge endings and bifurcations to match fingerprints rather than the overall pattern.
Sw week12-b
Fingerprint scanners work in two main ways - optical scanners capture an image of ridges and valleys using light, while capacitive scanners detect ridges and valleys through electric fields. Both methods capture fingerprint features like ridge endings and bifurcations, which fingerprint analysis systems then use to identify matching fingerprints in a database rather than comparing entire fingerprint images. Fingerprint patterns are unique due to ridges developing as an evolutionary advantage for gripping.
Sw week12-b
Fingerprint scanners work in two main ways - optical scanners capture an image of ridges and valleys using light, while capacitive scanners detect ridges and valleys through electric fields. Both capture the unique pattern to identify individuals. Fingerprint analysis focuses on distinctive minutiae points like ridge endings and bifurcations rather than the overall print, as specific features provide a more accurate match than the complete pattern.
Fingerprint
Fingerprint identification is commonly used for biometric identification. Fingerprints are analyzed by their patterns (loops, whorls, arches) and ridge characteristics (minutiae). Most automatic fingerprint identification systems (AFIS) match minutiae features. The document proposes using soft computing techniques like neural networks and fuzzy logic to improve an AFIS. It describes a 5-layer network that uses minutiae extraction from divided fingerprint blocks to classify fingerprints hierarchically and reduce errors. Potential applications include security access, mobile unlock systems, driver's licenses, and national ID programs.
Fingerprint Based Biometric ATM Authentication System
Fingerprint Based Biometric ATM Authentication SystemInternational Journal of Engineering Inventions www.ijeijournal.com
This document summarizes a research paper on implementing a fingerprint-based biometric authentication system for ATMs using a PIC microcontroller. It describes how fingerprint identification works by analyzing ridge and valley patterns. The system uses a PIC16F877A microcontroller to collect fingerprint data from a fingerprint sensor module and match it to an enrolled fingerprint template to authenticate users. If a match is found, the ATM cashbox opens, and if not, an alarm sounds. The document discusses the methodology, advantages, limitations and components of the system, including the fingerprint sensor, microcontroller, LCD display, motor driver, and buzzer.How Fingerprint Scanner Work
Fingerprint scanners provide biometric security by verifying a user's identity based on their unique fingerprint ridges and valleys. Optical scanners use light reflection to generate an image of ridges and valleys, while capacitive scanners use electric fields. Both generate images of the fingerprint pattern. The scanner then analyzes and compares specific fingerprint features called minutiae points, like ridge endings and bifurcations, between the scanned fingerprint and stored fingerprints to identify a match. By matching a sufficient number of common minutiae points, the scanner can determine if two fingerprints belong to the same individual for authentication purposes.
Nikolas Perrault | Inside the Creation of the Neural Network that can Unlock ...
Nikolas Perrault discusses the new neural network that is able to create fake digital fingerprints to unlock fingerprint secure devices.
GANNON UNIVERSITYELECTR.docx
GANNON UNIVERSITY
ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT
FALL2015
GECE 572: DIGITAL SIGNAL PROCESSING
FINGER PRINT RECOGNITION USING MINUTIAE BASED FEATURE
FINAL PROJECT
Prepared by
THADASINA PRUTHVIN REDDY
[email protected]
SALMAN SIDDIQUI
[email protected]
Instructor:
Dr. Ram Sundaram
Table of contents
1. Abstract
2. Introduction
3. Fingerprint matching
4. Pre-processing stage
5. Minutiae extraction stage
6. Post-processing stage
7. Merits & Demerits
8. Applications & future scope
9. Conclusions
10.References
1. Abstract
Nowadays, conventional identification methods such as driver's license, passport, ATM cards and PIN codes do not meet the demands of this wide scale connectivity. Automated biometrics in general, and automated fingerprint authentication in particular, provide efficient solutions to these modern identification problems. Fingerprints have been used for many centuries as a means of identifying people. The fingerprints of individual are unique and are stay unchanged during the life time. Fingerprint matching techniques can be placed into two categories, minutiae-based and correlation based. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality the correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.
2. Introduction
Biometric recognition refers to the use of distinctive physiological (e.g. fingerprint, palm print, iris, face) and behavioral (e.g. gait, signature) characteristics, called biometric identifiers for recognizing individuals.
Fingerprint recognition is one of the oldest and most reliable biometric used for personal identification. Fingerprint recognition has been used for over 100 years now and has come a long way from tedious manual fingerprint matching. The ancient procedure of matching fingerprints manually was extremely cumbersome and time-consuming and required skilled personnel.
Finger skin is made up of friction ridges and sweat pores all along these ridges. Friction ridges are created during fetal life and only the general shape is genetically defined. The distinguishing nature of physical characteristics of a person is due to both the inherent individual genetic diversity within the human population as well as the random processes affecting the development of the embryo. Friction ridges ...
Fingerprint recognition (term paper) Project
This document discusses fingerprint recognition. It begins by defining fingerprint recognition as the automated process of verifying a match between two fingerprints. Fingerprints are a form of biometrics used to identify individuals due to their uniqueness. The document then discusses how fingerprints are distinguished by features called minutiae, specifically ridge endings and bifurcations. It also outlines some common fingerprint matching techniques such as correlation-based and minutiae-based matching.
Biometric and cyber security 1
- Biometrics such as fingerprints, iris scans, and facial recognition are increasingly being used for security and identification purposes in applications like access control, e-commerce, banking, and computer login to enhance security. Fingerprint recognition is a mature biometric technique used in this research.
- However, biometrics also present cybersecurity risks if the biometric data is stolen or systems are circumvented. Hackers could potentially force individuals to provide biometric samples or use facsimiles to gain unauthorized access to systems. Proper management of biometric data and systems is needed to mitigate risks.
L026070074
The document is a research paper that studies using a neural network model for fingerprint recognition. It discusses how fingerprint recognition is an important technique for security and restricting intruders. The paper proposes using an artificial neural network with backpropagation training to recognize fingerprints. It describes collecting fingerprint images, classifying them, enhancing the images, and training the neural network to match images and recognize fingerprints with high accuracy. The methodology, implementation, and results of using a backpropagation neural network for fingerprint recognition are analyzed.
Fingerprint recognition using minutiae based feature
Fingerprint recognition techniques can be categorized as minutiae-based or correlation-based. Minutiae-based techniques extract and match minutiae points like ridge endings and bifurcations. This approach has difficulties with low quality fingerprints. Correlation-based techniques match ridge patterns but require precise image registration. Fingerprint matching algorithms first enhance images, extract minutiae points, and compare points between images to determine a match.
Finger print authentication for bikes
The document discusses fingerprint-based authentication for embedded systems security. It begins with an introduction describing how embedded systems are increasingly networked and vulnerable. It then discusses fingerprint identification and authentication techniques. The rest of the document describes the hardware and software requirements for a fingerprint authentication system, including a microcontroller, fingerprint module, LCD, power supply, and other components. Block diagrams and working principles are provided to explain how the system functions to authenticate users via fingerprint and control access.
Applications of Biometrics in Technology
Biometric in the field of Computer science ! This is a powerpoint presentation prepared as a first year participation in college presentation competition. Topic - Applications of biometrics in technology. This was my first attempt. Hope it comes in use for people in need of a simple presentation.
Biometric technology
This document discusses various biometric technologies including fingerprint recognition, iris scanning, retina scanning, voice recognition, signature verification, face recognition, and hand geometry recognition. It describes how each type of biometric works, including capturing biometric data, extracting distinguishing features, enrollment, verification, and matching against stored templates. Biometrics are increasingly used for identification and access control because they cannot be lost, stolen, or forgotten like ID cards or passwords. However, biometric systems must also account for changes in biometrics over time.
Roy
Fingerprint scanners work by capturing an image of a fingerprint using either optical or capacitance sensors. The image is then analyzed and compared to stored fingerprints to authenticate identity. While fingerprint scanners provide convenient authentication, a major downside is that stolen fingerprint data cannot be replaced like passwords, posing significant risks if identity is compromised.
Sw2assignment
Fingerprint scanners have become more common for security and access. They work by capturing an image of a fingerprint's unique ridge and valley patterns using either optical or capacitive sensors. The scanner then analyzes and compares these minutiae points to fingerprints stored in its database to authenticate identity. While fingerprint authentication has advantages over traditional passwords, there are also privacy concerns since fingerprints cannot be changed if compromised.
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Fingerprint Based Biometric ATM Authentication System
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Nikolas Perrault | Inside the Creation of the Neural Network that can Unlock ...
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Fingerprint recognition using minutiae based feature
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
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Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
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The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Infrastructure Challenges in Scaling RAG with Custom AI models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Week12
- 1. How Fingerprint Scanners Work s1170021 Yumi Sato
- 2. 1. Introduction to How Fingerprint Scanners Work You can pick up a personal USB fingerprint scanner for less than $100, and just like that, your computer's guarded by high-tech biometrics. A password, you need your distinctive print to gain access. In this article, we'll examine the secrets behind this exciting development in law enforcement and identity security.
- 3. 2. Fingerprint Basics Fingerprints are one of those bizarre twists of nature. These ridges form through a combination of genetic and environmental factors. And while two prints may look basically the same at a glance, a trained investigator or an advanced piece of software can pick out clear, defined differences. A fingerprint scanner's job is to take the place of a human analyst by collecting a print sample and comparing it to other samples on record.
- 4. 3. Analysis Most fingerprint scanner systems compare specific features of the fingerprint, generally known as minutiae. Typically, human and computer investigators concentrate on points where ridge lines end or where one ridge splits into two (bifurcations). Collectively, these and other distinctive features are sometimes called typica. In the print on record, it simply has to find a sufficient number of minutiae patterns that the two prints have in common.
- 5. 5 key words fingerprint, distinctive, twists, ridges, features
- 6. 3 sentences, Why are these sentences important? In this article, we'll examine the secrets behind this exciting development in law enforcement and identity security. Fingerprints are one of those bizarre twists of nature. Most fingerprint scanner systems compare specific features of the fingerprint, generally known as minutiae. These sentence are general statement about the research paper, background, to the research paper, and analysis.
- 7. web presentation link http://computer.howstuffworks.com/fingerprint- scanner.htm More Great Links: * Fingerprint Analysis - The Basics * Dermatoglyphics * Impact of Artificial "Gummy" Fingers on Fingerprint Systems * The History of Fingerprints * Taking Fingerprints