This document discusses two perspectives on biometrics that reveal limitations to realizing its potential benefits in combating identity fraud, organized crime, and terrorism.
1) The value chain perspective explains that large-scale, compulsory biometric systems for general use allow successful infringements to go unnoticed as they spread. A biometric system is only manageable if it is essential for solving a dominant problem within its value chain.
2) The identity fraud perspective shows that our focus on identity documents, rather than the person and situation, facilitates fraud by making verification procedures predictable. Biometrics will only be effective if it introduces less predictable verification and greater risks of detection for fraudsters.
Standardized, large-scale
This document summarizes a research paper that proposes using iris recognition and palm vein technology for credit card authentication as a more secure alternative to existing authentication methods. The paper outlines some of the limitations of current authentication methods like PINs, signatures, and fingerprints. It then describes how the proposed system would work, using iris recognition to verify a user's identity followed by palm vein scanning for authentication. The document explains the technical details of how iris recognition and palm vein scanning extract unique biometric patterns and compares them to stored templates to authenticate users. It claims the detection rate of fraud using this dual biometrics approach would be 99.995% compared to traditional methods.
Biometrics system penetration in mobile devicesSwapnil Jagtap
Biometrics is the automated identification or verification of human identity through the measurement of repeatable physiological or behavioral characteristics.
This document provides an overview of biometric encryption, which is a technique that securely links a digital cryptographic key to a biometric template (such as a fingerprint image) during enrollment. It allows the key to be retrieved later during verification by matching a new biometric sample to the stored template. The key is independent of the biometric data, so compromising the key does not invalidate the use of that biometric, and the key can be easily updated. The document describes how biometric encryption uses correlation filtering of fingerprint images to generate a "bioscrypt" during enrollment that securely embeds the key, and then retrieves the key during verification by correlating a new fingerprint with the bioscrypt. It outlines the requirements for distortion tolerance
This document provides an overview of blind authentication, a secure crypto-biometric authentication protocol. It begins with an introduction discussing the primary concerns with biometric authentication systems, such as template protection, user privacy, trust issues between users and servers, and network security. It then reviews related work on addressing these concerns. The document proceeds to explain the blind authentication process, which involves feature extraction, enrollment with a trusted third party, and authentication between a user and server. Features of blind authentication include strong encryption for template protection and privacy while maintaining authentication accuracy. Advantages are template security, non-repudiation, and revocability.
This document provides an introduction to biometric security systems. It discusses how biometrics has evolved from early manual criminal identification techniques to modern automated systems. It describes some of the first commercial biometric devices used over 25 years ago for timekeeping and access control. It then summarizes several common biometric modalities used in existing security systems, including fingerprint, face, signature, voice, and gait recognition.
A Comparison Based Study on Biometrics for Human RecognitionIOSR Journals
Abstract: A biometric system provides automatic recognition of an individual based on a unique feature or
characteristic possessed by the individual. These biometric characteristic may physiological or behavioral.
Unlike other identification methods such as id proof, tokens and password, the distinct aspect of biometric
recognition comes into light from randomly distributed features in human being. In this paper, I describe the
novel comparison based upon various aspects to make easy selection for biometric device deployment in specific
environment. This paper proposes a comparison among all kind of biometric system available in the society.
The existing computer security systems used at various places like banking, passport, credit cards, smart cards,
PIN , access control and network security are using username and passwords for person identification.
Biometric systems also introduce an aspect of user convenience; it means one can be authorized by representing
himself or herself. In this paper, the main focus is on working principal of biometric technique, the various
biometrics systems and their comparisons.
Keywords: Biometrics, authentication, identification, recognition
Credit Card Duplication and Crime Prevention Using BiometricsIOSR Journals
1. The document proposes using iris recognition and palm vein technology for credit card authentication as a way to improve security over existing methods.
2. Current authentication methods like PINs, signatures, and fingerprints have vulnerabilities like being observable and reproducible.
3. The proposed system uses iris recognition followed by palm vein scanning, comparing the biometric data to stored templates to authenticate the user. If both comparisons match, the transaction would be allowed.
4. Iris patterns and palm vein patterns are unique to each individual and difficult to reproduce, providing improved security over existing authentication methods.
Multimodal Biometric endorsement for secure Internet banking using Skin Spect...IRJET Journal
This document proposes a multimodal biometric authentication system for secure internet banking using skin spectroscopy, finger knuckle texture, and finger nail recognition. It discusses the limitations of unimodal biometric systems and how a multimodal approach combining three biometrics—skin spectroscopy, knuckle texture, and nail plate analysis—can improve accuracy and security. The system aims to provide stronger authentication for internet banking transactions compared to traditional password or token-based systems.
This document summarizes a research paper that proposes using iris recognition and palm vein technology for credit card authentication as a more secure alternative to existing authentication methods. The paper outlines some of the limitations of current authentication methods like PINs, signatures, and fingerprints. It then describes how the proposed system would work, using iris recognition to verify a user's identity followed by palm vein scanning for authentication. The document explains the technical details of how iris recognition and palm vein scanning extract unique biometric patterns and compares them to stored templates to authenticate users. It claims the detection rate of fraud using this dual biometrics approach would be 99.995% compared to traditional methods.
Biometrics system penetration in mobile devicesSwapnil Jagtap
Biometrics is the automated identification or verification of human identity through the measurement of repeatable physiological or behavioral characteristics.
This document provides an overview of biometric encryption, which is a technique that securely links a digital cryptographic key to a biometric template (such as a fingerprint image) during enrollment. It allows the key to be retrieved later during verification by matching a new biometric sample to the stored template. The key is independent of the biometric data, so compromising the key does not invalidate the use of that biometric, and the key can be easily updated. The document describes how biometric encryption uses correlation filtering of fingerprint images to generate a "bioscrypt" during enrollment that securely embeds the key, and then retrieves the key during verification by correlating a new fingerprint with the bioscrypt. It outlines the requirements for distortion tolerance
This document provides an overview of blind authentication, a secure crypto-biometric authentication protocol. It begins with an introduction discussing the primary concerns with biometric authentication systems, such as template protection, user privacy, trust issues between users and servers, and network security. It then reviews related work on addressing these concerns. The document proceeds to explain the blind authentication process, which involves feature extraction, enrollment with a trusted third party, and authentication between a user and server. Features of blind authentication include strong encryption for template protection and privacy while maintaining authentication accuracy. Advantages are template security, non-repudiation, and revocability.
This document provides an introduction to biometric security systems. It discusses how biometrics has evolved from early manual criminal identification techniques to modern automated systems. It describes some of the first commercial biometric devices used over 25 years ago for timekeeping and access control. It then summarizes several common biometric modalities used in existing security systems, including fingerprint, face, signature, voice, and gait recognition.
A Comparison Based Study on Biometrics for Human RecognitionIOSR Journals
Abstract: A biometric system provides automatic recognition of an individual based on a unique feature or
characteristic possessed by the individual. These biometric characteristic may physiological or behavioral.
Unlike other identification methods such as id proof, tokens and password, the distinct aspect of biometric
recognition comes into light from randomly distributed features in human being. In this paper, I describe the
novel comparison based upon various aspects to make easy selection for biometric device deployment in specific
environment. This paper proposes a comparison among all kind of biometric system available in the society.
The existing computer security systems used at various places like banking, passport, credit cards, smart cards,
PIN , access control and network security are using username and passwords for person identification.
Biometric systems also introduce an aspect of user convenience; it means one can be authorized by representing
himself or herself. In this paper, the main focus is on working principal of biometric technique, the various
biometrics systems and their comparisons.
Keywords: Biometrics, authentication, identification, recognition
Credit Card Duplication and Crime Prevention Using BiometricsIOSR Journals
1. The document proposes using iris recognition and palm vein technology for credit card authentication as a way to improve security over existing methods.
2. Current authentication methods like PINs, signatures, and fingerprints have vulnerabilities like being observable and reproducible.
3. The proposed system uses iris recognition followed by palm vein scanning, comparing the biometric data to stored templates to authenticate the user. If both comparisons match, the transaction would be allowed.
4. Iris patterns and palm vein patterns are unique to each individual and difficult to reproduce, providing improved security over existing authentication methods.
Multimodal Biometric endorsement for secure Internet banking using Skin Spect...IRJET Journal
This document proposes a multimodal biometric authentication system for secure internet banking using skin spectroscopy, finger knuckle texture, and finger nail recognition. It discusses the limitations of unimodal biometric systems and how a multimodal approach combining three biometrics—skin spectroscopy, knuckle texture, and nail plate analysis—can improve accuracy and security. The system aims to provide stronger authentication for internet banking transactions compared to traditional password or token-based systems.
The document discusses the use of biometric authentication technologies like fingerprints for ATMs in rural India. It notes that biometric ATMs can help illiterate and barely literate rural residents access banking services by establishing their identity through biometrics rather than passwords. The document outlines some companies in India that are developing biometric solutions for ATMs, including fingerprint authentication systems. It also discusses the benefits of biometric ATMs, how biometric authentication systems work, and provides examples of different biometric technologies like fingerprint recognition and face recognition.
This document summarizes a research study that investigated using fingerprint biometrics to improve security for automated teller machines (ATMs). The study proposed embedding a fingerprint authentication scheme into ATMs to supplement the existing personal identification number (PIN) security. A survey was conducted with bank customers and students in Nigeria to examine perceptions of ATM reliability and the reliability of fingerprint biometrics. The results indicated support for using fingerprints to authenticate ATM users as a way to address security issues with PINs, such as PINs being stolen or easily guessed. The document reviewed related work on biometric identifiers for security and discussed materials and methods used in the study.
The document discusses various topics related to biometrics including:
1. Biometrics can be used for physical access control, ATM access, and authenticating transactions over the telephone or from home computers. Fingerprints, iris scans, and facial recognition are some common biometric technologies used.
2. Early biometrics systems from the 1880s involved precise body measurements and physical descriptions but failed by 1903. Modern biometrics use automated methods to recognize individuals based on physiological or behavioral characteristics.
3. Biometrics are part of identity management and can help with security, national security threats, accountability, and optimizing resources. However, biometrics also have limitations like environmental factors affecting performance.
This document is a report on biometric sensors written by Arhind Kumar Gautam for his Information Technology department. It contains an introduction to biometric sensors, a history of biometrics, definitions of biometric terms, descriptions of different types of biometric sensors including physical (fingerprint, face recognition, retina scan) and behavioral (voice recognition, signature) biometrics. It also discusses future applications of biometrics and provides a conclusion on biometric sensors.
A brief overview of biometric authentication and the benefits it can have on your business and its overall security. Is biometric authentication something you should be looking into? Find out now...
The document discusses biometrics, which uses biological characteristics to identify individuals. It describes the basic architecture of a biometric system, including enrollment, verification, and identification. Common biometric technologies are fingerprint, hand, voice, retina, and facial recognition. Applications include physical access control, computer login, banking, and voting. Benefits are increased security and convenience, while drawbacks include high costs, potential privacy issues, and the system not being foolproof. The conclusion is that biometrics will likely become more widely used after addressing current limitations.
Biometric encryption is a technique that securely binds a cryptographic key to a biometric template. During enrollment, a random key is linked to a user's biometric template using a binding algorithm. During verification, applying the same biometric will regenerate the key through a retrieval algorithm. This allows authentication while preventing storage of the actual biometric data. Biometric encryption offers advantages like improved security, ability to revoke compromised keys, and privacy by not retaining biometric images.
What Is Facial Recognition, How It Is Used & What Is It’s Future Scope?Kavika Roy
1. Facial recognition technology works by identifying 80 nodal points on a human face and mapping variables like nose length, eye size, and cheekbone shape to create a unique "faceprint".
2. It can quickly and reliably recognize individuals when conditions are optimal, but performance decreases if the face is blurred, in shadow, or not facing forward.
3. Facial recognition is used for smartphone security like Face ID, airport security, law enforcement matching mugshots to databases, social media tagging photos, and targeted digital advertising based on demographics.
MULTIMODAL BIOMETRIC AUTHENTICATION: SECURED ENCRYPTION OF IRIS USING FINGERP...ijcisjournal
Securing data storage using biometrics is the current trend. Different physiological as well as behavioral biometrics like face, fingerprint, iris, Gait, voice etc.. is used in providing security to the data. The proposed work explains about the biometric encryption technology which will securely generate a digital key using two biometric modalities. Iris is encrypted using Fingerprint ID of 32-bit as the key in this work.
For encryption Blowfish algorithm is used and the encrypted template is stored in the database and one is given to the user. During the authentication time user input the template and the fingerprint. This template is then decrypted and verified with the original template taken from the database to check whether the user is genuine or an imposter. Hamming distance is used to measure the matching of the templates. CASIA Iris
database is used for experimentation and fingerprint images read through the R303 - fingerprint reader.
Personal authentication using 3 d finger geometry (synopsis)Mumbai Academisc
The document proposes a 3D finger geometry authentication system as an improvement over existing hand geometry authentication methods. It captures 3D information using a low-cost depth sensor to limit constraints on hand placement. It extracts 3D geometry points from the finger and uses those as features for authentication, achieving similar accuracy to other methods but with greater convenience. It segments the finger, finds boundary points, enlarges the image, and extracts geometry points along circles projected inward from the boundary to generate the feature vector for matching. Preliminary results found the proposed system comparable to state-of-the-art hand geometry recognition.
This document summarizes biometrics for secure e-transactions using mobile phones. It proposes a multi-biometric model that integrates voice, fingerprint, and facial scanning embedded in a mobile phone to make e-transactions more secure. It discusses how each biometric like facial recognition, fingerprint scanning, and voice recognition works and their practical challenges. Integrating multiple biometrics improves security but also has issues like some populations like the elderly having unreadable biometrics.
The document discusses biometric devices, including what they are, common types of biometric devices, and how they work. It provides examples of biometric identifiers like fingerprints, DNA, and voice patterns. It also discusses how biometric devices are used for identification and access control in places like educational institutions, hospitals, offices, shopping malls, and housing societies. The document outlines some of the benefits of biometric devices, but also notes potential privacy and security concerns with storing individuals' biometric data.
This document discusses research progress in mobile fingerprint template protection. It covers three main schemes: biometric key generation, fuzzy schemes, and non-invertible transforms. Biometric key generation aims to directly derive cryptographic keys from fingerprints to avoid storing biometric features or secret keys. Fuzzy schemes hide secrets within public information so keys can be retrieved through biometric matching. Non-invertible transforms store transformed biometric features instead of the original template. The document analyzes the advantages and limitations of different schemes and points out open issues for future research in mobile fingerprint template protection.
This document proposes a system for strengthening security for online banking transactions. It involves multi-level authentication including face recognition, graphical OTP authentication using a 4x4 grid of random numbers, and security questions. Users first register security images, a security pattern by selecting indexes on a 4x4 grid, answers to security questions, and their face is recorded. For login, the security images and username/password are verified. Transactions require face recognition if a webcam is available, otherwise graphical OTP authentication is used where the user selects numbers from the indexes of their security pattern on a randomly generated 4x4 grid. Additionally, two random security questions are asked before completing a transaction. The system aims to provide secure electronic transactions through this multi-factor
Ppt on use of biomatrix in secure e trasactionDevyani Vaidya
Biometrics refers to authentication techniques that rely on measurable physiological and individual characteristics to automatically verify identity. There are two main types of biometrics: physiological, which relate to the body shape like fingerprints, retina, and face; and behavioral, which relate to behaviors like voice, handwriting, and typing patterns. Biometric systems use verification to compare a sample to a single stored template or identification to search a sample against a database of templates to resolve a person's identity. While biometrics can provide strong authentication for applications like secure banking, border control, and access control, they are not perfect and have limitations like cost, accuracy, and privacy concerns.
The document discusses biometric systems for security. It defines biometrics as measuring biological traits to identify individuals. It then discusses the history of biometrics using fingerprinting in China in the 14th century. It describes the main types of biometric devices as behavioral (e.g. voice, signature) or physical (e.g. fingerprint, face) and lists their common uses including banking, attendance tracking, and data security. Finally, it compares biometric security to other methods and outlines some limitations such as noise in data and variations over time.
A SECURITY BASED VOTING SYSTEMUSING BIOMETRICIJERA Editor
The problem of voting is still critical in terms of safety and security. This paper is about the
design and development of a voting system using fingerprint to provide a high performance with high security
to the voting system. Fingerprint biometrics is widely used for identification. Biometrics identifiers cannot be
misplaced and they represent any individual identity. The integration of biometric with electronic voting
machine requires less manpower, save much time of voters and personal, ensure accuracy,transparency and fast
result in election. In this paper a framework for electronic voting machine based on biometric verification is
proposed and implemented. The proposed framework provides secured identification and authentication
processes for the voters and candidates through the use of fingerprint biometric
Biometrics Authentication of Fingerprint with Using Fingerprint Reader and Mi...TELKOMNIKA JOURNAL
The idea of security is as old as humanity itself. Between oldest methods of security were
included simple mechanical locks whose authentication element was the key. At first, a universal–simple
type, later unique for each lock. A long time had mechanical locks been the sole option for protection
against unauthorized access. The boom of biometrics has come in the 20th century, and especially in
recent years, biometrics is much expanded in the various areas of our life. Opposite of traditional security
methods such as passwords, access cards, and hardware keys, it offers many benefits. The main benefits
are the uniqueness and the impossibility of their loss. The main benefits are the uniqueness and the
impossibility of their loss. Therefore we focussed in this paper on the the design of low cost biometric
fingerprint system and subsequent implementation of this system in praxtise. Our main goal was to create
a system that is capable of recognizing fingerprints from a user and then processing them. The main part
of this system is the microcontroller Arduino Yun with an external interface to the scan of the fingerprint
with a name Adafruit R305 (special reader). This microcontroller communicates with the external database,
which ensures the exchange of data between Arduino Yun and user application. This application was
created for (currently) most widespread mobile operating system-Android.
Foundations for Search and Social Strategies, SES London 2014Jonathan Beeston
The changing world of search and social media, and how you can create a framework for a united search and social media strategy. Presentation from Search Engine Strategies, London 2014.
The document discusses the use of biometric authentication technologies like fingerprints for ATMs in rural India. It notes that biometric ATMs can help illiterate and barely literate rural residents access banking services by establishing their identity through biometrics rather than passwords. The document outlines some companies in India that are developing biometric solutions for ATMs, including fingerprint authentication systems. It also discusses the benefits of biometric ATMs, how biometric authentication systems work, and provides examples of different biometric technologies like fingerprint recognition and face recognition.
This document summarizes a research study that investigated using fingerprint biometrics to improve security for automated teller machines (ATMs). The study proposed embedding a fingerprint authentication scheme into ATMs to supplement the existing personal identification number (PIN) security. A survey was conducted with bank customers and students in Nigeria to examine perceptions of ATM reliability and the reliability of fingerprint biometrics. The results indicated support for using fingerprints to authenticate ATM users as a way to address security issues with PINs, such as PINs being stolen or easily guessed. The document reviewed related work on biometric identifiers for security and discussed materials and methods used in the study.
The document discusses various topics related to biometrics including:
1. Biometrics can be used for physical access control, ATM access, and authenticating transactions over the telephone or from home computers. Fingerprints, iris scans, and facial recognition are some common biometric technologies used.
2. Early biometrics systems from the 1880s involved precise body measurements and physical descriptions but failed by 1903. Modern biometrics use automated methods to recognize individuals based on physiological or behavioral characteristics.
3. Biometrics are part of identity management and can help with security, national security threats, accountability, and optimizing resources. However, biometrics also have limitations like environmental factors affecting performance.
This document is a report on biometric sensors written by Arhind Kumar Gautam for his Information Technology department. It contains an introduction to biometric sensors, a history of biometrics, definitions of biometric terms, descriptions of different types of biometric sensors including physical (fingerprint, face recognition, retina scan) and behavioral (voice recognition, signature) biometrics. It also discusses future applications of biometrics and provides a conclusion on biometric sensors.
A brief overview of biometric authentication and the benefits it can have on your business and its overall security. Is biometric authentication something you should be looking into? Find out now...
The document discusses biometrics, which uses biological characteristics to identify individuals. It describes the basic architecture of a biometric system, including enrollment, verification, and identification. Common biometric technologies are fingerprint, hand, voice, retina, and facial recognition. Applications include physical access control, computer login, banking, and voting. Benefits are increased security and convenience, while drawbacks include high costs, potential privacy issues, and the system not being foolproof. The conclusion is that biometrics will likely become more widely used after addressing current limitations.
Biometric encryption is a technique that securely binds a cryptographic key to a biometric template. During enrollment, a random key is linked to a user's biometric template using a binding algorithm. During verification, applying the same biometric will regenerate the key through a retrieval algorithm. This allows authentication while preventing storage of the actual biometric data. Biometric encryption offers advantages like improved security, ability to revoke compromised keys, and privacy by not retaining biometric images.
What Is Facial Recognition, How It Is Used & What Is It’s Future Scope?Kavika Roy
1. Facial recognition technology works by identifying 80 nodal points on a human face and mapping variables like nose length, eye size, and cheekbone shape to create a unique "faceprint".
2. It can quickly and reliably recognize individuals when conditions are optimal, but performance decreases if the face is blurred, in shadow, or not facing forward.
3. Facial recognition is used for smartphone security like Face ID, airport security, law enforcement matching mugshots to databases, social media tagging photos, and targeted digital advertising based on demographics.
MULTIMODAL BIOMETRIC AUTHENTICATION: SECURED ENCRYPTION OF IRIS USING FINGERP...ijcisjournal
Securing data storage using biometrics is the current trend. Different physiological as well as behavioral biometrics like face, fingerprint, iris, Gait, voice etc.. is used in providing security to the data. The proposed work explains about the biometric encryption technology which will securely generate a digital key using two biometric modalities. Iris is encrypted using Fingerprint ID of 32-bit as the key in this work.
For encryption Blowfish algorithm is used and the encrypted template is stored in the database and one is given to the user. During the authentication time user input the template and the fingerprint. This template is then decrypted and verified with the original template taken from the database to check whether the user is genuine or an imposter. Hamming distance is used to measure the matching of the templates. CASIA Iris
database is used for experimentation and fingerprint images read through the R303 - fingerprint reader.
Personal authentication using 3 d finger geometry (synopsis)Mumbai Academisc
The document proposes a 3D finger geometry authentication system as an improvement over existing hand geometry authentication methods. It captures 3D information using a low-cost depth sensor to limit constraints on hand placement. It extracts 3D geometry points from the finger and uses those as features for authentication, achieving similar accuracy to other methods but with greater convenience. It segments the finger, finds boundary points, enlarges the image, and extracts geometry points along circles projected inward from the boundary to generate the feature vector for matching. Preliminary results found the proposed system comparable to state-of-the-art hand geometry recognition.
This document summarizes biometrics for secure e-transactions using mobile phones. It proposes a multi-biometric model that integrates voice, fingerprint, and facial scanning embedded in a mobile phone to make e-transactions more secure. It discusses how each biometric like facial recognition, fingerprint scanning, and voice recognition works and their practical challenges. Integrating multiple biometrics improves security but also has issues like some populations like the elderly having unreadable biometrics.
The document discusses biometric devices, including what they are, common types of biometric devices, and how they work. It provides examples of biometric identifiers like fingerprints, DNA, and voice patterns. It also discusses how biometric devices are used for identification and access control in places like educational institutions, hospitals, offices, shopping malls, and housing societies. The document outlines some of the benefits of biometric devices, but also notes potential privacy and security concerns with storing individuals' biometric data.
This document discusses research progress in mobile fingerprint template protection. It covers three main schemes: biometric key generation, fuzzy schemes, and non-invertible transforms. Biometric key generation aims to directly derive cryptographic keys from fingerprints to avoid storing biometric features or secret keys. Fuzzy schemes hide secrets within public information so keys can be retrieved through biometric matching. Non-invertible transforms store transformed biometric features instead of the original template. The document analyzes the advantages and limitations of different schemes and points out open issues for future research in mobile fingerprint template protection.
This document proposes a system for strengthening security for online banking transactions. It involves multi-level authentication including face recognition, graphical OTP authentication using a 4x4 grid of random numbers, and security questions. Users first register security images, a security pattern by selecting indexes on a 4x4 grid, answers to security questions, and their face is recorded. For login, the security images and username/password are verified. Transactions require face recognition if a webcam is available, otherwise graphical OTP authentication is used where the user selects numbers from the indexes of their security pattern on a randomly generated 4x4 grid. Additionally, two random security questions are asked before completing a transaction. The system aims to provide secure electronic transactions through this multi-factor
Ppt on use of biomatrix in secure e trasactionDevyani Vaidya
Biometrics refers to authentication techniques that rely on measurable physiological and individual characteristics to automatically verify identity. There are two main types of biometrics: physiological, which relate to the body shape like fingerprints, retina, and face; and behavioral, which relate to behaviors like voice, handwriting, and typing patterns. Biometric systems use verification to compare a sample to a single stored template or identification to search a sample against a database of templates to resolve a person's identity. While biometrics can provide strong authentication for applications like secure banking, border control, and access control, they are not perfect and have limitations like cost, accuracy, and privacy concerns.
The document discusses biometric systems for security. It defines biometrics as measuring biological traits to identify individuals. It then discusses the history of biometrics using fingerprinting in China in the 14th century. It describes the main types of biometric devices as behavioral (e.g. voice, signature) or physical (e.g. fingerprint, face) and lists their common uses including banking, attendance tracking, and data security. Finally, it compares biometric security to other methods and outlines some limitations such as noise in data and variations over time.
A SECURITY BASED VOTING SYSTEMUSING BIOMETRICIJERA Editor
The problem of voting is still critical in terms of safety and security. This paper is about the
design and development of a voting system using fingerprint to provide a high performance with high security
to the voting system. Fingerprint biometrics is widely used for identification. Biometrics identifiers cannot be
misplaced and they represent any individual identity. The integration of biometric with electronic voting
machine requires less manpower, save much time of voters and personal, ensure accuracy,transparency and fast
result in election. In this paper a framework for electronic voting machine based on biometric verification is
proposed and implemented. The proposed framework provides secured identification and authentication
processes for the voters and candidates through the use of fingerprint biometric
Biometrics Authentication of Fingerprint with Using Fingerprint Reader and Mi...TELKOMNIKA JOURNAL
The idea of security is as old as humanity itself. Between oldest methods of security were
included simple mechanical locks whose authentication element was the key. At first, a universal–simple
type, later unique for each lock. A long time had mechanical locks been the sole option for protection
against unauthorized access. The boom of biometrics has come in the 20th century, and especially in
recent years, biometrics is much expanded in the various areas of our life. Opposite of traditional security
methods such as passwords, access cards, and hardware keys, it offers many benefits. The main benefits
are the uniqueness and the impossibility of their loss. The main benefits are the uniqueness and the
impossibility of their loss. Therefore we focussed in this paper on the the design of low cost biometric
fingerprint system and subsequent implementation of this system in praxtise. Our main goal was to create
a system that is capable of recognizing fingerprints from a user and then processing them. The main part
of this system is the microcontroller Arduino Yun with an external interface to the scan of the fingerprint
with a name Adafruit R305 (special reader). This microcontroller communicates with the external database,
which ensures the exchange of data between Arduino Yun and user application. This application was
created for (currently) most widespread mobile operating system-Android.
Foundations for Search and Social Strategies, SES London 2014Jonathan Beeston
The changing world of search and social media, and how you can create a framework for a united search and social media strategy. Presentation from Search Engine Strategies, London 2014.
3 things to start this afternoon to improve your paid searchJonathan Beeston
The document recommends 3 things to improve paid search: 1) test new ad formats to keep up with changes, 2) act differently for mobile vs desktop as their behaviors differ, and 3) attribute traffic across channels and optimize spending based on their models.
Enrichir Ses Méthodes Avec des Processus Unifiés AgilesRomain Couturier
Les méthodes agiles mettent au placard de nombreuses méthodes de projet dont les défauts (lourdeur, incompréhension des attentes finales, manque de priorités) ont marqué de leur empreinte l\'échec ou le demi-succès des projets. Longtemps associé à cette image, le “Processus Unifié” s\'accorde pourtant avec le manifeste agile et complète les méthodes connues comme Scrum ou XP sur les moyens et gros projets. Cette session vous propose de parcourir d’autres interprétations et applications du “Processus Unifié” à travers ses différentes versions simplifiées et agiles : Agile Unified Process, Open UP et EssUP.
With the growth of technology their grows threat to our data which is just secured by passwords so to make it more secure biometrics came into existence. As biometric systems are adopted and accepted for security purpose for various information and security systems. Hence it is immune to attacks. This paper deals with the security of biometric details of individuals. In this paper we will be discussing about biometrics and its types and the threats and security issues which is not talked about usually. The different technologies evolved and had contributed to biometrics in long run and their effects. Sushmita Raulo | Saurabh Gawade "Security Issues Related to Biometrics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44951.pdf Paper URL: https://www.ijtsrd.com/computer-science/computer-security/44951/security-issues-related-to-biometrics/sushmita-raulo
Biometric authentication uses unique human physical and behavioral characteristics for authentication purposes. Physical biometrics include fingerprints, facial patterns, iris scans, and retinal patterns. Behavioral biometrics analyze keystrokes, gait, voice, mouse movements, signatures, and cognition. Biometrics provide stronger authentication than passwords alone but have disadvantages like inability to change compromised biometrics and potential for "master fingerprints" to trick some devices. Biometrics are increasingly used for consumer, government, and corporate authentication.
The document discusses the use of biometrics in Aadhar cards, PAN cards, and fingerprint lock apps in India. It provides background on each type of identification and how biometrics were integrated. For Aadhar cards, fingerprints and iris scans were added to uniquely identify individuals and prevent fraud. This increased security and convenience. PAN cards were also linked to Aadhar cards and now use biometrics like fingerprints to authenticate identity. Fingerprint lock apps use biometrics to securely lock phones based on unique fingerprints, improving privacy. Biometrics thus enhance security and authentication across many identification systems in India.
Facial Feature Recognition Using Biometricsijbuiiir1
Face recognition is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness. Biometric requires no physical interaction on behalf of the user. Biometric allows to perform passive identification in a one to many environments. Passwords and PINs are hard to remember and can be stolen or guessed; cards, tokens, keys and the like can be misplaced, forgotten, purloined or duplicated; magnetic cards can become corrupted and unreadable. However individuals biological traits cannot be misplaced, forgotten, stolen or forged.
Biometrics is the most secure and suitable authentication tool. It is the automated method of recognizing a person based on a physiological or behavioral characteristic. Biometric authentication is used in computer Science for verifying human identity.
- 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.
You raise some valid concerns about potential issues with face recognition technologies. Here are a few thoughts in response:
1. Convenience must be balanced with privacy and security. Facial recognition is useful in some contexts, but sensitive data also needs strong protections.
2. Multiple authentication factors are preferable to a single biometric like facial recognition alone. Combining facial recognition with passwords, pins, fingerprints, etc. provides redundancy to address situations like an unconscious phone owner.
3. Emergency access features could be built in. For example, phones could have a emergency override mode that bypasses facial recognition if certain conditions are met, like multiple failed attempts or an SOS call is placed.
4. Context matters greatly. Facial
Biometric technologies use biological features such as fingerprints, veins, faces and irises to identify individuals. They greatly improve the accuracy and reliability of identification and verification systems by taking out the element of human error. In the area of public safety, biometric technologies in the form of fingerprinting, iris and facial recognition have made a significant contribution to border control and law enforcement. Brought to you by NEC. To find out more, do visit http://www.nec.com/safety
Biometrics can be used to improve cybersecurity by integrating biometric authentication into daily operations. Biometrics uses unique physical traits like fingerprints, facial recognition, or iris scans to verify a user's identity. While biometrics provides convenient authentication as physical traits are difficult to steal or forget, there are also privacy concerns over collection and potential misuse of biometric data without user consent. The document discusses various biometric technologies, their applications, benefits for cybersecurity, and challenges regarding privacy and potential workarounds to strengthen biometric data protection.
This presentation discusses biometrics technology, including its definition, history, current uses, and future applications. Biometrics refers to identifying individuals by their unique physical or behavioral traits, and modern biometrics uses characteristics like iris patterns, fingerprints, facial features, and voiceprints. While early forms of biometrics identification have existed for centuries, technologies have advanced significantly in recent decades. Biometrics is now widely used for security and access control across various industries and applications. Looking ahead, the presentation argues biometrics should replace outdated password systems and be further integrated into infrastructure, with appropriate security and privacy protections.
The document discusses biometrics and provides context on its history and uses. Biometrics is defined as using human physical characteristics to verify identity, and has been used since ancient times, though recent technological advances have enabled more sophisticated biometric identification systems. The document explores the evolution of biometrics technology over time, from early uses of fingerprints to modern applications like iris scans and DNA analysis. It also examines the advantages and limitations of different biometric methods.
Biometric technology uses unique human characteristics to identify or verify individuals. Common biometrics include fingerprints, iris patterns, voice recognition and hand geometry. The history of biometrics began in the 1850s with fingerprint identification. A biometric system works by capturing a sample, extracting features, creating a template and matching it to existing templates. Benefits are that biometrics cannot be lost or stolen, while disadvantages include privacy issues and reliability depending on environmental factors. Current applications include border control, driver's licenses, banking and access control. Biometrics is expected to grow in use for security and transactions.
This document presents an overview of biometric technologies. It defines biometrics as the science of measuring and analyzing biological data for authentication purposes, such as fingerprints, iris patterns, and voice. The document outlines the basic components of biometric systems including readers, software, and databases. It also categorizes biometrics as physiological (face, fingerprints, etc.) or behavioral (signature, voice). The document discusses several biometric modalities like fingerprint, face, iris and voice recognition and their uses. It covers issues around privacy, cancelable biometrics, and soft biometrics. Overall, the document provides a high-level introduction to biometrics and their applications.
This document provides an introduction to biometric authentication. It discusses how biometric authentication uses physical and behavioral characteristics to identify individuals. Some benefits of biometric authentication over traditional methods like passwords are that biometric traits cannot be lost or stolen. The document then describes various biometric modalities (or traits) that can be used, including fingerprints, iris scans, facial recognition, DNA, gait, signatures, voice, and keystrokes. It also discusses how biometric systems work and potential sources of error in biometric identification.
Application de Reconnaissance faciale - eigenfaces, fisherfaces et lbphSarra ERRREGUI
This document is a student's final year project report for obtaining a Bachelor's degree in Mathematical and Computer Science, specializing in databases. The project is about developing a facial recognition application. It presents an overview of the security issues the project aims to address. It then outlines the chapters which will cover the state of the art in facial recognition, methods like Eigenfaces, Fisherfaces and LBP, the experimentation and results. The conclusion will summarize the results and discuss future work.
The document discusses biometrics, which is the automated identification or verification of human identity through physiological and behavioral traits. It covers the history of biometrics, different biometric categories like fingerprints, iris scans, and voice recognition. It also discusses identification versus authentication modes, accuracy metrics, applications in various sectors, advantages, disadvantages and limitations of biometrics. The conclusion is that while biometrics provide strong authentication, a balance between security and privacy needs to be achieved as technologies advance.
Antispoofing in face biometrics: A comprehensive study on software-based tech...CSITiaesprime
The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.
The document discusses biometrics, which is the study of methods for uniquely recognizing humans based on physical and behavioral traits. Some examples of physiological biometrics are fingerprint, face recognition, DNA, hand and palm geometry, and iris recognition. Behavioral biometrics include typing rhythm, gait, and voice. The document then explains the process of biometric systems which involves capturing biometric data, creating a template, storing it in a database, and comparing new captures against stored templates to authenticate users. It discusses some challenges with biometric technologies including privacy issues, discrimination concerns, and the permanence of biometrics.
Behavioral biometrics is the field of study connected with the proportion of extraordinarily recognizing and quantifiable examples in human exercises. The term stands out from actual biometrics, which includes inborn human attributes, for example, fingerprints or iris designs.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
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
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UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
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!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
1. 1
Two barriers to realizing the benefits of biometrics*
A chain perspective on biometrics, and identity fraud as biometrics’ real challenge
Jan Grijpink**
ABSTRACT
Along at least twelve dimensions biometric systems might vary. We need to exploit this variety to manoeuvre biometrics
into place to be able to realise its social potential. Subsequently, two perspectives on biometrics are proposed revealing
that biometrics will probably be ineffective in combating identity fraud, organised crime and terrorism:
the value chain perspective explains the first barrier: our strong preference for large scale biometric systems for
general compulsory use. These biometric systems cause successful infringements to spread unnoticed. A biometric
system will only function adequately if biometrics is indispensable for solving the dominant chain problem. Multi-
chain use of biometrics takes it beyond the boundaries of good manageability.
the identity fraud perspective exposes the second barrier: our traditional approach to identity verification. We focus
on identity documents, neglecting the person and the situation involved. Moreover, western legal cultures have made
identity verification procedures known, transparent, uniform and predictable. Thus, we have developed a blind spot
to identity fraud. Biometrics provides good potential to better checking persons, but will probably be used to
enhance identity documents. Biometrics will only pay off if it confronts the identity fraudster with less predictable
verification processes and more risks of his identity fraud being spotted. Standardised large scale applications of
biometrics for general compulsory use without countervailing measures will probably produce the reverse.
This contribution tentatively presents a few headlines for an overall biometrics strategy that could better resist identity
fraud.
1. INTRODUCTION
Biometrics is using your body as a digital key to get access to secured services and data, or using a physical characteristic
to automatically recognise somebody. These days, information technology makes it possible to quickly digitise physical
characteristics so that we can either depict them as an image or subject themto calculations which result in numbers, so -
called biometric templates. This can be done with the contour of a hand or a finger, a fingerprint or the pattern of an iris
or a retina.
1. fingerprint
2. finger geometry
3. hand geometry
4. pattern of an iris
5. vein pattern in the retina
6. face recognition
7. voice recognition
8. dynamic signature (speed and pressurewhile signing)
9. rhythmof typingcertain words on a keyboard
Fig. 1. Examples of biometric characteristics
* This articlewas originally publishedin: Optical SecurityandCounterfeit Deterrence Techniques V, editedby Rudolf L. van Renesse, Proceedings
of SPIE-IS&T Electronic Imaging, SPIEVol. 5310,pp.90-102(2004).
** Dr mr J.H.A.M. Grijpink,economist andlawyer by educationandinformationstrategist by profession, is Principle Advisor at the Strategy
Development Department ofthe Netherlands Ministryof Justice.In 1975he obtaineda postgraduate degree in management consultancy at the
Stichtinginteracademiale opleidingorganisatiekunde (SIOO) and in 1997 his Ph.D. at the Technical Universityof Eindhoven.He is a Certified
Management Consultant (CMC) anda RegisteredInformation Expert (RI).
j.grijpink@minjus.nl; www.keteninformatisering.nl (articles in English included)
2. 2
Even variable physical characteristics can be used for biometric identification of an individual, such as his voice, the way
he moves his hand when writing his signature, or his rhythm when typing certain words on a keyboard. See Fig. 1.
This contribution deals with the use of biometric data with the purpose of checking someone’s identity during
transactions (admission, ordering, payment, agreement) using computerised systems,on the spot and instantaneous, both
for the protection of individuals and for the prevention of identity fraud and crime. Certain biometric technologies such
as DNA are not (yet) suitable for this purpose, and are therefore not addressed here. The use of biometrics in criminal
proceedings for the identification of mortal remains and in investigations into people's origin, descent or health also fall
beyond the scope of this contribution.
Two examples illustrate the biometrics domain covered by this contribution:
Example 1
The first example is an off-line local application using an anonymous biometric template without a link to an identity document.
This application is designed to prevent people from remaining in the building (e.g. a museum or a bank) after it closes. The
biometric template is linked to a one-off pin code selected by the visitor himself. This pin code is necessary because the 'point of
reference' of a unique chipcard number is not available in this application. The self-selected pin code indicates which templates
have to be mutually compared. Without that pin code a comparison of templates is not reliable, because measurements of the same
physical characteristics of the same person always show minor differences. For this reason, two measurement values that are in
close proximity to each other do not necessarily originate from the same person. Because this application does not register any
other information about the visitor and theone-off pin code is selected by the person himself, the visitor's anonymity is guaranteed.
The application works as follows. A visitor can only enter the building once he has entered the self-selected pin code and the
access computer has calculated a biometric template based on the shape of his hand, for instance. He will only be able to leave the
building again once he has re-entered his pin code and the computer has not detected any major deviation between the biometric
measurements related to that pin code upon his arrival and departure. Thecomputer then deletes both the self-selected pin code and
the two biometric templates. A check is made to establish whether all readings have been deleted every evening when the museum
closes. If not, all visitors to the museum have not yet left the building. If a visitor is indeed found in the building after closing time,
it is possible to establish whether that person is the one that the museum staff are looking for by checking with the biometric
characteristic that remains in the computer.
By using an anonymous biometric detail we can electronically, without human intervention, establish that the same person has
arrived and departed, without needing to know the exact identity of the visitor concerned. For the significance of biometrics for the
protection of privacy, it is important to be aware that anonymous biometrics can be just as accurate and safe as personalised and
semi-anonymous biometrics.
Example 2
This example is an application at an airport that is designed to detect suspect luggage, and to prevent people from being supplanted
between the moment they check in and the moment they board the aircraft. This application makes use of personalised biometrics
relating to some types of document. The application works as follows. At the moment that the passenger checks in, his biometric
template is calculated and stored in a temporary file for the flight in question together with the (unique) numbers of the boarding
pass and any luggage labels. When the passenger is due to board the aircraft, as part of the safety check the template is recalculated
and compared with the template in the temporary file that accompanies that particular boarding pass. If the discrepancy between
the measured values is too big, it is desirable to conduct a further identity check to establish whether that person has been
supplanted. If the comparison shows that this is not the case, the biometric data are immediately deleted. If, at the time of
departure, there is still an unmatched biometric detail left in the temporary register, the accompanying luggage can be traced and
taken off the aircraft. This luggage apparently does not belong to one of the passengers who have boarded, and represents a safety
risk. Because boarding passes are issued in people's names once proof of identity has been verified, this application makes use of
personalised biometrics. However, if the personalised biometric data are deleted immediately after they have been used, this
application of biometrics does not invade people's privacy. The advantage of personalised biometrics in this application is that in
cases of doubts about someone's identity or unattended luggage, the airport staff immediately know whom they need to look for.
In the event of a deliberate act, the biometric characteristic left behind can be used to prove who was involved in the incident.
Biometrics derives its significance from the person based nature of the physical characteristic that serves as the point of
recognition. In comparison with the customary non-person-based methods to check someone’s identity such as pin codes,
passwords, electronic signature or encryption keys, biometrics therefore has a number of general advantages, which -
depending on the application - make the recognition of an individual person accurate whilst simultaneously safeguarding
against fraud and protecting people's privacy. Biometrics can be used to verify a person’s identity even if his true identity
is not or cannot be known. In these cases biometrics makes it possible to determine whether a person is the same person
as the one you expect. Many social processes require exactly this and can do without knowing someone’s true identity.
These general advantages are however accompanied by some disadvantages.The technology is sensitive to fraud not only
3. 3
with regard to the equipment, but to organisation and procedures as well. Because a biometric reading does not yield
precisely the same image or template on each occasion, biometrics does not provide complete certainty. The recognition
quality of a biometric application depends on appropriate parameter setting by which one can state which measurement
discrepancies are acceptable to be still able to assume that the values originate fromthe same person. The more accurately
we configure the calculation, the less chance there will be that somebody else is considered to be me, but the chance of
my not being recognised increases accordingly! The advantages of biometrics over human visual checks are however of
overriding importance if we want to guarantee people's privacy or if one may expect many human recognition errors in a
concrete situation. For instance, if one has to check large numbers of people quickly, over a long period of time or froma
distance. Neither are biometric techniques subject to recognition errors due to faulty observation resulting from
preconceptions, distraction or tiredness.
This contribution presents two perspectives on biometrics to reveal how we unknowingly limit its potential social value
in our information society. The result will probably be that biometrics will prove to be ineffective in combating identity
fraud, organised crime or terrorism. These two perspectives are the value chain perspective and the perspective derived
from the phenomenon of identity fraud.
The value chain perspective explains the first barrier to realising the benefits of biometrics: our strong preference for
large scale biometric systems for general use. General use of biometrics causes successful infringements to spread
unnoticed. It will be argued that a biometric system will only function adequately within the boundaries of a value
chain, and only if it is indispensable for solving that particular value chain’s dominant problem (see reference 2 for
an explanation of the theory behind the value chain perspective). In a value chain thousands of independent
organisations work together to realise a social product, e.g. healthy food, less crime, faultless health care, safe travel,
a value chain being a temporary co-operation between these organisations focussed on the solution of their dominant
chain problem. This is a chain wide problem that puts the whole value chain product at risk, no individual chain
partner being able to solve it on its own. This tie of any biometric systemwith its value chain means that general use
of a biometric system makes it more difficult to manage and lets successful infringements unnoticeably spread (see
reference 2 for an explanation of this phenomenon for a comparable identity instrument, a personal number). Only if
biometric security provisions or biometric person recognition are indispensable for solving a chain's dominant
problem, can that biometric system be managed adequately. As long as we underestimate the strength of the ties
between tailor made biometric solutions and their value chains and focus on standardised large scale application of
biometrics for general use, biometrics will not be effective in combating identity fraud, organised crime and
terrorism. This perspective will be explored further in chapter 3.
The identity fraud perspective exposes the second barrier to realising the benefits of biometrics: our traditional
approach to identity verification. We focus on identity documents, neglecting the person and the situation involved.
The latter differs from situation to situation, from value chain to value chain. This implies that the process of identity
checking is rapidly becoming the main issue and the way we make use of the great variety of possible biometric
applications to develop tailor-made solutions (see reference 3). With our traditional western legal-administrative
approach to identity documents we unintentionally facilitate identity fraud by our pursuit of simplicity, uniformity
and transparency. This has made our identity verification procedures step by step more known, transparent, uniform
and predictable, enabling the identity fraudster to predict where, when, how and by whom his identity will be
checked. The element of surprise is only enjoyed by the identity fraudster. Thus, we have developed a blind spot to
identity fraud. Moreover, identity verification procedures are often public and can be inconspicuously observed in
order to establish weak points in the technology, the organisation or the procedures. With a certain amount of prepa-
ration, an identity fraudster can outwit most identity checks (see reference 4). Biometrics will only pay off if it
confronts the identity fraudster with less predictable verification processes and more risks of his identity fraud being
spotted. As long as we use biometric solutions to enhance the quality of identity documents instead of the quality of
the verification processes and focus on standardised large scale application of biometrics for general use on identity
documents, biometrics will not be effective in combating identity fraud, organised crime and terrorism. This
perspective will be explored in chapter 4.
But first we will explore in chapter 2 the enormous variety of biometric applications for person recogn ition and identity
verification to understand better the way tailor made biometric solutions are able to effectively solve major chain
problems, or to prevent identity fraud.
4. 4
Biometrics provides both protection against crime and against intrusion on one’s privacy. The public interest in
biometrics is growing and the technology is making rapid progress. An information society needs identity checking using
person related characteristics. Therefore, biometrics can be expected to play an important role in the long term. Because
the development of new technology usually goes through bad patches, a well considered strategy is necessary for the
application of biometrics. Such a strategy should be based on the insights that diversified use of biometrics in a number
of important value chains determines whether biometrics will live up to its promises of security and safety and privacy
protection and that biometrics can only prevent identity fraud to happen, if it is used to improve the identity checking
process in a specific situational context instead of simply adding to the features of an identity document only.
2. THE VARIETY OF BIOMETRIC SYSTEMS FOR TAILOR-MADE SOLUTIONS
This chapter explains the enormous variety of possible biometric solutions, using a number of features and aspects of
biometrics that can be combined in various ways to realise different designs.
1. Identification or verification
2. Inclusion or exclusion
3. A lot of techniques using different physicalcharacteristics (variable or fixed)
4. Image of a physicalcharacteristic or a templatecalculated from it
5. Function
6. Scope of application
7. Unicity of the physicalcharacteristic
8. Central or de-centralised storage of biometric data
9. Small-scale or large-scale application
10. Open or closed target group
11. Anonymous, semi-anonymous or biometric data by name ('personalised')
12. Voluntary or compulsory participation
Fig. 2. Features of biometric systems
Biometrics calls for tailor-made solutions, no single biometric technique nor form of application is suitable for every
problem relating to identity checking. This is important because we need different tailor-made solutions for different
dominant chain problems and we need to prevent identity fraud by a large number of small-scale, sector-restricted
applications. These offer a double advantage to the protection of privacy and the prevention of crime: one can reduce the
consequences of an identity fraud that has not been detected to a minimum, and subsequently have more opportunities to
unmask a successful impostor. In addition, sector-restricted applications offer more legal protection against unlimited
linking of personal details using biometric templates. Within a small-scale application the voluntary use of biometrics can
be more easily handled because non-biometric exceptions for people who really object to this method of checking one’s
identity can be better controlled. Fig. 2. gives a picture of twelve different features of biometric applications that can be
used to realise tailor-made solutions. The list is not meant to be exhaustive.
A. Identification method
The implications of biometrically checking people’s identities for their privacy and protection against fraud is
significantly determined by the method used for the recognition of an individual. We make two different distinctions:
1. Identification orverification
This distinction relates to the envisaged knowledge of a person's identity.
There are two alternatives:
a) establishing precisely who someone is (identification);
b) establishing whether a person is the same person as expected (verification).
The establishment of a person's true identity involves a thorough investigation into someone’s identity, which is rarely
done in the Netherlands outside of criminal law enforcement and immigration. It is deemed sufficient to establish
5. 5
whether a person is the same person as expected, by ascertaining whether several pieces of information belong to the
same person, for example. Verification is less far-reaching than identification because we remain unsure whether a
person actually is who he says he is. But in many social situations this is sufficient.
2. Inclusion or exclusion
This distinction between inclusion and exclusion relates to another basic approach to checking someone’s identity:
a) we can positively establish whether someone is indeed the right person.
This is referred to as the 'inclusive' use of a recognition technique;
b) we can negatively establish that someone is not the right person.
This is referred to as the 'exclusive' use of a recognition technique.
A single point of difference is sufficient to exclude someone with one hundred percent certainty! On the other hand,
inclusive use never gives one hundred percent certainty, even though that certainty does of cours e grow with each point
of similarity that is ascertained. When using only one biometric template, it is only possible to perform an 'inclusive'
check. If it is necessary to establish immediately that someone is not the right person (exclusion), at least two different
biometric characteristics have to be used. After all, in a case of mistaken identity, the chance of two physical
characteristics being relatively just as close together is virtually ruled out.
The 'exclusive' use of a biometric characteristic to establish that someone is not the same person barely penetrates into
people's private lives, but inclusively establishing a person's identity does. Information technology does in fact make the
least radical form of checking someone’s identity biometrically, the exclusive use of anonymous biometrics, technically
feasible. Therefore, from a privacy perspective it is notable that most people unnecessarily show a spontaneous
preference for the most radical form, the inclusive use of personalised biometrics.
B. Biometric technique
3. A lot of techniquesusing different physical characteristics (variable or fixed)
Fixed physical characteristics can be more successfully imitated than variable characteristics. A person can also himself
change a variable physical characteristic without this being noticed. This is useful if a person is under threat, but will
generally fail to offer an adequate solution for situations in which it pays to pass oneself off as two different people. If
considerable advantage can be gained by doing this, it is better to use an unchangeable physical characteristic.
4. Image of a physical characteristic or a template calculated from it
With an image of someone else's finger tip, a person can pass himself off as someone else when checked, for ins tance to
spuriously suggest this person's involvement in a transaction or action. These situations can not as easily occur if use is
made of a template. This is a biometric number calculated from unique features found in a fingerprint, for instance.
Because the other information contained in the image is not used in the calculation, it is not possible to retrospectively
calculate the original image of the fingerprint from the biometric number (template). There are countries that (want to)
forbid the central storage of biometric images, or impose stricter regulations on the central storage of biometric images
than will apply to the central storage of templates.
C. Implementation
5. Function
Biometric images or templates fulfil all sorts of functions, often several at the same time. The ability to recognise a
person as the right one is in itself an important function, but using a biometric characteristic one can also establish that
two details belong to each other, e.g. for verification purposes. Linking or checking personal details with a biometric
characteristic related to that particular person is more reliable than with any other not person related given such as a
number, a word (name) or an image (photograph, signature or logo). Comparing details with biometrics can prevent or
reveal errors. The biometric detail can prevent confusion if other identifying personal details are not unique, for instance
if a foreign name can be spelled in more than one way. In combination with other data, biometrics enhance the ability to
manage databases and combat data contamination or fraud. It is important in this context that one can check whether
somebody wrongly has more than one identity at his disposal or that an identity is wrongly being used by more than one
person.
6. Scope of application
Biometric systems have a certain scope of application, in terms of both content and geographical area. Some are only
used within a specific sectoror value chain (examples are the use of biometrics in the health service or in the penal law
enforcement chain). Other systems are more general, because they are used in several sectors orchains. Biometric
systems can be local (e.g. a biometric systemfor building access), regional, national or international. An example of the
last category of biometric systems is a biometric passport.
6. 6
7. Unicity of the physical characteristic
The unicity of a physical chracteristic has apart from a physical dimension two additional ones: time and place.
Generally, a fingerprint is regarded as unique, although some races have fingerprints with cannot be measured because
the relief is too low. The uniqueness of many other physical characteristics is uncertain. Fortunately, absolute uniqueness
is not a prerequisite for every application of biometrics. If the target group is relatively small or if the person is expected
back at the checkpoint within a short period of time after first admission, the use of a less than unique characteristic can
result in an accurate person recognition.
8. Central or de-centralised storage of biometric data
At the one extreme, there is the decentralised storage of a single biometric detail on a single chipcard placed in the hands
of the person from whom the biometric detail originates. The other extreme consists ofthe storage of all biometric data in
a central file for on-line checking without the use of a chipcard. The biometric details can be physically concentrated at
the same location, but this is not necessary, ‘central’ in this context means that all stored biometric details can be directly
accessed and compared with each other. This makes it possible to establish, for example, whether a person is already
included in the file, but under a different name.
9. Small-scale or large-scale application
In a small-scale application, the uniqueness of the physical characteristic is less important because the chance of two
individuals having virtually the same physical characteristic reduces significantly as the target group b ecomes smaller. In
large-scale applications deliberately passing oneself off as someone else tends to be more successful and more valuable.
This is not so much due to the technique itself as to the number of people, the vast organisation and extensive procedures
involved. Small-scale applications are less risky and tend to increase privacy, particularly if the applications make use of
different biometric techniques. This way one can also limit the damage of an undetected identity fraud.
10. Open or closed target group
A closed target group is one in which every member is known beforehand. In a closed group the chance of two virtually
identical measurements that could possibly lead to mistaking a person's identity is smaller than with open target groups.
Within closed groups a simple application using anonymous biometrics yields a check in which an outsider has very little
chance of successfully passing himself off as one of the members of the group.
11. Anonymous, semi-anonymous or biometric data by name ('personalised')
If biometric data can only be traced back to the person from whom the measured value originated with disproportionate
effort, the biometric data are anonymous data and cannot be regarded as personal data. A good example of anonymous
biometrics is dirty glassware in a restaurant. It is a hopeless task to trace a fingerprint on one of the glasses to a restaurant
diner who has already left. One will never be able to find him. This could explain why we don't concern ou rselves too
much with this glassware in practice. To assess the possibilities to find the person from whom a biometric given
originates, we have to look at the application as a whole rather than only at the biometric detail on its own. The situation
is different when it comes to personalised biometrics, in which case the biometric detail is attributed to a specific person
referred to by name, or the name can be established with some - but not disproportionate - effort. Semi-anonymous are
biometric data of which only one party (person, organisation) knows from whom the biometric characteristic originates,
e.g. the issuer of a biometric chipcard. Other parties do not. In their eyes the biometric data are anonymous. With a semi-
anonymous biometric chipcard one’s identity can only be checked anonymously by other parties, thus accurately
establishing that the cardholder is the right person without knowing who he is.
12. Voluntary or compulsory
Compulsory use of biometrics by the government or by private bodies requires a legal basis. In the European Union the
voluntary use of personalised biometrics is in principle permitted, unless the biometric characteristic can be used to
establish someone's race or origin. But is voluntary co-operation always truly voluntary? This is not solely determined by
market conditions, but also by the existence of a fully-fledged alternative facility without biometrics, so that true freedom
of choice is possible. If on a really voluntary basis, even a public authority may use biometrics without such use being
explicitly provided for by the law.
These twelve aspects illustrate that the design of a biometric application involves a large number of implicit choices that
determine the effectiveness of the application, its sensitivity to fraud and its consequences for personal safety and
security. A wide range of different applications is possible. If applied on a large scale, most biometric techniques are
vulnerable to fraud and privacy breaches currently. It is important to bear in mind that - unlike the situation in the
analogue era - it is not the verifier but the person being verified who has the upper hand. After all, he is one hundred per
cent certain of being able to activate an emergency procedure, by imperceptibly using someone else's chipcard, for
example, or by pretending that he has lost his card. Therefore, from the perspective of protecting people's privacy and
7. 7
security in an information society, anonymous and semi-anonymous biometric applications in particular will in the future
have major significance. ‘Anonymous’ meaning that it is not reasonably possible to establish who somebody is, ‘semi-
anonymous’ that nobody can find out who you are with the exception of a trusted party, e.g. the issuing authority of an
identity instrument. Small, ‘exclusive’, (semi-)anonymous biometric systems, combining the identification methods of
exclusion (‘that is not him’) and verification (‘he is the same person, but I don't know who he is’), are very promising for
a safe and free information society especially if the target group can be considered to be a closed group. In these cases
even simple technology and procedures are sufficient. Biometrics will not realise its full social significance until we
recognise and utilise the wide-ranging possibilities offered by the anonymous or semi-anonymous use of biometrics.
3. A VALUE CHAIN PERSPECTIVE ON BIOMETRICS
In this chapter a value chain perspective on biometrics is presented consisting ofthree principles:
a) A dynamic chain concept based on a dominant chain problem as the trigger of temporary chain co-operation;
b) The role of this dominant chain problem within the irrational context of a value chain;
c) A multi-level focus on a value chain.
a) A dynamic chain concept based on a dominant chain problemas the trigger of temporary chain co -operation
A value chain can be seen as a temporary pattern of co-operation around a dominant chain problem. A dominant chain
problem is a recurring operational problem that is vehemently felt in every link of a value chain, no chain partner being
able to adequately tackle this problem on his own. Solving this recurring operational problem is exactly what brings
about the structural collaboration between the parties involved. As soon as the dominant chain problem fades away or
shifts towards a different problem, the value chain re-arranges itself to better cope with the new dominant chain problem.
This idea of a shifting chain problem implies a rather unusual dynamic concept of a value chain. However, it can better
explain the peculiar dynamics of more complex social value chains, such as the chain co-operation in the stockbreeding
chain to make sure that the meat is of no threat to your health or in the asylum chain to guarantee that undocumented
asylum seekers can only have one new identity (henceforth the ‘right’ one!) in the European Union.
b) The role of this dominant chain problemwithin an irrational context
The second component of our chain perspective is the absence of an all-encompassing authority in a value chain which
causes a decision-making process at the chain level to be poorly organised and irrational. Problems and solutions are
randomly connected in a decision-making process that is only partly known or ambiguous. This randomly connectedness
is meant by ‘irrational’. The chain partners repeatedly find themselves placed in unexpected situations. As long as a
specific dominant chain problem is keeping the chain firmly in its grip, this chain problem functions as the real 'boss' in
the value chain. As soon as this dominant chain problem loses its impact, the chain resolves itself or regroups around a
new dominant chain problem. This idea of an irrational chain environment is useful to biometric systems, because it
implies that a biometric system depends on the ups and downs of the dominant chain problemrequiring biometrics to be
adequately solved. Only as long as the dominant chain problem really triggers the co-operation within this value chain,
can the quality of its biometric systembe counted on.
c) Multi-level focus on a value chain
The third underlying principle of the presented chain perspective consists of a multi-level focus on chains. Fig. 3
visualises this multi-level focus illustrating how biometric systems can be positioned at three levels with regard to a
specific value chain: (1) at the ‘base’ of a value chain, (2) at a collective level within the boundaries of a value chain, to
be indicated by ‘chain level’ and (3) at a general supra-chain level above several value chains.
A biometric system at the ‘base’ of a value chain refers to an organisation’s internal biometric system, for instance to
control access to a vulnerable building or installation. These biometric systems can also be used by one or more partners
in the chain in their bilateral communication. Chain biometrics, on the other hand, forms part of the information
infrastructure of a value chain and can be seen as positioned at ‘chain level’. This means that the biometric system is
managed collectively by or on behalf of all organisations in the value chain, i.e. independently of individual chain
partners. Chain biometrics supports and steers the exchange of information between collaborating organisations in the
chain from the collective ‘chain level’. General biometric systems are positioned at a level above several value chains to
be used by organisations in these chains, with different functions and patterns of use in each of these chains. Thus,
general biometric systems are not chain-related.
8. 8
base level of a
value chain
internal source register
chain level
chain information system
connection between source register and chain information system
legend:
internal source
registers
chain information system
connection between source registers
internal biometrics
chain biometrics
biometrics for
general use
supra-chain level
connection between chain information system and general information system
general information system
general information system
Fig. 3. Biometric systems at three levels with regard to value chains
Chain biometrics
A biometric system must primarily be up to its function in a specific value chain. This, after all, is what determines the
requirements for the biometric application and the volume of fraud that could threaten the correct functioning of the
chain. A short distance between chain biometrics and its dominant chain problem yields a wide range of chain -related,
self-cleaning mechanisms. Those mechanisms work best when the biometric system is absolutely indispen sable for
resolving the dominant chain problem. Only then will the biometric system maintain itself for a longer period of time at
chain level. If an existing chain biometric system is no longer necessary to solve this chain’s particular chain problem,
the chain partners are no longer motivated to properly manage their biometric system together and actively monitor it
against misuse and fraud. It then ‘sinks’ to the base level of a chain for internal use, if it is kept alive at all. The starting
point for a biometrics strategy is therefore that a stable biometric systemis intrinsically tied to a value chain. This system
can only be stable as a chain biometric systemif tied to a dominant chain problem that cannot do without it.
Supra-chain biometrics
Supra-chain biometrics is chracterised by multi-chain use of the same biometric system. Such biometric systems are more
difficult to manage and more vulnerable to contamination and fraud. Because biometrics is still in an early stage of
development, this point can best be explained with a concrete and topical example from a related phenomenon, personal
number systems: the Dutch social security number. Up until the middle of the nineteen-eighties, the (original) taxnumber
was only used internally by the Dutch tax authorities. It was not given out to other authorities nor used by employers.
Since 1985 it has been a public number, and since 1988 it has also been used by the social security sector, re-christened
as the social security number. In this sector the social security number began to function as a framework for registering,
accessing and linking details about work, social security premiums, benefits and facilities. It was not legally permitted to
use the social security number outside of those two sectors. That changed in the middle of the nineties; a start was made
in 1996 with placing the social security number on the passport and driving licence, two important Dutch ID documents.
A wide range of bodies, both within and outside of the public sector, can now legally take it over from an ID document
that has been presented to them and use it (internally). In 2001 the social security number was legally designated as the
9. 9
education number for the verification of the financing of schools and other applications in the education area. Finally, a
political decision is expected in the near future regarding the question of whether the social security number (possibly in
encrypted form) will also be used in the healthcare sector as a unique care identification number (CIN). Using the CIN
for access purposes to medical files, it will eventually be possible to put in place a virtual patient file for all. Fig. 4
summarises the gradual development of the social security number: from confidential internal number, through
increasing multichain use to one of the current candidates for a compulsory general personal number. It can be assumed
that biometric systems in future will show similar growth patterns as personal numbers.
Fig. 4. Development of theDutch social security number
Why do we tend to opt for a general supra-chain biometric system even if we accept its natural ties with a value chain?
Firstly, we underestimate the costs of sharing a biometric system like it has been for personal numbers. People often do
not have a clear picture in mind of the increasing management problem and costs because of the multi-chain use. After
all, a general supra-chain system plays a different role in each of the chains that are using it. In each chain it is affected
by chain-specific sources of contamination and forms of fraud. In the case of multi-chain use, this leads to unexpected
management problems because people in the one chain have no idea about the specific contamination sources and forms
of fraud in other chains that are making use of the supra-chain system. Multi-chain use increases the value of the supra-
chain biometric detail, which makes abusing it more attractive. Moreover, supra-chain biometric systems are less easy to
gear up to the diverse requirements of various chains that make use of the system. This is why supra-chain biometric
systems are basically more difficult to manage without the cleaning mechanisms triggered by an unambiguously focussed
collaboration and enforced by the whip of its dominant chain problem. The requirements fromvarious chains can be very
diverse, making it worth while to examine which forms of management are available for a biometric s ystem.
In Fig. 5. a distinction is made horizontally between passive and active management, and the scope of the management
activities is given vertically, from issuing a biometric instrument to monitoring its use. This gives rise to six different
management forms for biometric systems. Depending on the application and value of the biometric detail, every chain
features some general and some chain-specific sources of contamination and forms of fraud. A biometrics administrator
can prepare himself for this by making use of all chain-related self-cleaning mechanisms. Generally speaking, people will
therefore opt for the simplest and cheapest management form that meets the requirements. Management form 1, for
example, is usually adequate for temporary chain biometrics that represents no real threat to the enrolled persons,
whereas management form 6 is more appropriate for the management of a permanent public biometric system with a
substantial social value and a serious personal risk, e.g. the access to virtual medical files. Multi-chain usage yields a
different picture, if the chains set different requirements for the same biometric system and – accordingly – its
management. The requirements of the most vulnerable chain should be applied to a supra-chain biometric system, but
there is usually a lack of support for the costs of a high security management variant in chains which themselves set less
strict requirements.
Ministry Application Notes
Finance tax liability, tax payment confidential fiscal number until 1985, public thereafter
Social Affairs &
Employment
registration of employment, social security
premiums, benefits and facilities
since 1988 also used by the social security sector.
Home Affairs identity (social security number on passport and
driving licence)
since 1996 also used by the identity chain
Education, Science &
Culture
financing schools and other applications since 2001 also used by the education sector
Health, Welfare &
Sports
medical patient file will perhaps also be used by thehealthcare sector in
the future
10. 10
Passive Active
Issuing
1
enrollment on request without ID check
2
enrollment with legally prescribed ID check based on
documents and other data
Administration
3
registering holders' details
4
registering holders' details, and periodic check of the holder's
rights to prevent misuse
Monitoring
5
registering details about the use of the
biometric detail and registering misuse
or attempts at misuse
6
registering details about the use and registering misuse or
attempts at misuse, and combating misuse, before and after
Fig. 5. Six management forms for biometric systems
The starting point for a biometrics strategy is therefore that a stable biometric systemis tied to a value chain , functioning
well at chain level as long as the biometric system is indispensable for solving that particular value chain’s dominant
problem. Indeed, supra-chain biometrics can sometimes be effective and stable, but this will only be the case if the
biometric system is embedded in chain specific control systems geared to their own requirements. A socially robust
biometrics strategy should therefore be based on the insight that biometric systems are principally chain -tied and chain-
specific, and that multi-chain use of a biometric system generally makes a supra-chain biometric system unstable,
difficult to manage and vulnerable to contamination and fraud.
In the end, biometrics will undoubtedly be indispensable for person recognition and identity checks in our info rmation
society. But biometrics will only be up to this mission if we develop a great variety of tailor-made biometric systems and
pay much attention to their management and use. Administrators and politicians must get accustomed to the idea that a
complex information society needs a biometrics strategy with a multitude of high quality biometric systems. Chain bound
biometrics in a great variety of tailor-made solutions will be one of the pillars of the future information society. Effective
combating of identity fraud and misuse of biometric images and templates will decide on our safety and privacy in an
information society.
4. IDENTITY FRAUD AS BIOMETRICS’ MAJOR CHALLENGE
All over the world, in countries with highly different legal cultures, a great deal of effort is being spent improving the
identity management and tightening up the policy governing identity verification in order to enhance security. Countries
without or with only limited compulsory identification such as the Netherlands are now introducing a general compulsory
identification. Countries without laws for general compulsory proof of identity, such as the United Kingdom, are now
taking steps to introduce this identity instrument. Moreover, many countries are giving consideration to adding a
biometric feature of the holder to identity cards and travel documents. The purpose of a more stringent identity policy is
to enable the establishment of someone’s identity or the verification that the person involved is the right one in
increasingly more situations and with greater reliability. The importance of the distinction between these two forms of
identity check is that verifying that someone is the right person doesn’t necessarily require knowledge of who he is. Even
if the events of September 11 2001 had not taken place, this aim would still have gained more importance in keeping
with the growing number of transactions that are conducted electronically and at a distance without social control or
visual supervision. Electronic communication spans the whole world and in the coming years gains extra dimensions
through the increasing mobility and anonymity of society. Identity checks, too, are more and more likely to be performed
electronically and at a distance. This is why we will need a broad arsenal of identity instruments for person recognition
and identity verification in the future. In addition to what we already have, such as electronically readable identity cards,
pass numbers, personal numbers, pin codes and passwords, this will also have to include functions such as electronic
signatures and biometrics.
In the western world the identity policy concentrates mainly on combating document fraud: the use of coun terfeited legal
identity documents. The fraudulent use of a valid identity document officially belonging to someone closely resembling
the user (so called lookalike fraud) is already compelling us to face up to the problem of identity fraud. This means that
11. 11
somebody with dishonest intentions deliberately passes himself off under an identity that does not belong to him by using
the identity of someone else or a fictitious identity. Contrary to the current concept of identity fraud being the misuse of
an identity document, identity fraud in our definition doesn’t necessarily require the use of an identity document. The
identity fraudster can make use of personal numbers, photos, actions or occurrences as well, because they all feature an
identity suggestion from which people draw conclusions about whomthey are dealing with. Therefore, identity fraud can
take place anywhere and in many ways and is not restricted to specific situations, procedures or documents. Once a
person has fraudulently succeeded in being accepted as someone else, he can use this ‘identity’ in other situations along
regular channels. In these situations it usually is no longer possible to see through the preceding fraudulent identity
change.Identity fraud is often the first step towards a subsequent fraud,such as a bank fraud, a passport fraud or a benefit
fraud. But these more specific fraud labels distract our attention from the common denominator of these forms of fraud,
which is that a cunning method is used to deliberately misuse the identity of an existing or fictitious person. When an
identity fraud is successful the fraudster hides behind the other identity with all indications leading towards the victim, if
there is one, and not to the fraudster. Thus, a police investigation of a successful identity fraud remains often without
results.
Identity fraud as defined here, is forcing us to take a fresh look at identity instruments in our legal culture. It generally
turns out that measures and instruments that are useful in combating document fraud do not provide solutions to
combating identity fraud. Often they even have the reverse effect. Giving the social security number on the Dutch
passport is an example to illustrate this. Since this measure was introduced in 1996 it has provoked an enormous and still
growing volume of identity fraud. This identity measure was intended to make it easier for employers to know someone’s
social security number for various administrative purposes using identity documents for name-number verification. This
name-number verification is effective if the identity document is sound and the holder is the right person. But that is no
longer the case if it is used by someone else who closely resembles the rightful holder. After all, one should realise that a
name-number verification with an identity document always succeeds, regardless of who is making use of the document.
A look-alike person, who holds an identity document belonging to someone else, can assume that identity without being
noticed and thus have a free ride with that person’s social security number. Furthermore, there are many verification
situations in which one is asked to hand over a copy of an identity document. Any copy can easily be adapted for use by
somebody else. Giving the social security number in identity documents (e.g. the Dutch passport and driving licence)
therefore inadvertently makes identity fraud much easier by handing over to the fraudster the correct number together
with all the identifying personal details that belong to the rightful holder of the security number. The parallel with a
biometric detail is obvious. If an identity fraudster is able to find out the measurement value or image with which his
biometric details will be compared, there are many deceitful ways of misleading the automatic identity check. This
example underlines the need to test all existing identity measures for their effectiveness in combating identity fraud.
This example of the social security number on identity documents illustrates also that an identity fraudster is facilitated
by our western legal culture which requires regulations if public bodies enter the private domain of the civilian. These
regulations unintentionally enable him to predict where, when, how and by whomhis identity will be checked. Moreover,
identity verification procedures are often public and can be inconspicuously observed in order to establish weak points in
the technology, the organisation or the procedures. With a certain amount of preparation, an identity fraudster can outwit
most identity checks. Our legal-administrative approach has made the process of identity checking step by step more
transparent, uniform and predictable to the benefit of the identity fraudster. We ought to de-ritualise identity checks by
making them more varied and less predictable.
Two underlying principles hinder us as well when it comes to detecting or combating identity fraud:
Every identity card, personal number, PIN or biometric detail provides identity suggestions, which causes people to
assume they know whom they are dealing with. We spontaneously trust these administrative identities, even though
they are often based on unverified or unverifiable personal details. While checking someone’s identity we thus tend to
look at the identity document or personal number without independently screening its identity suggestion. This also
applies to a biometric device: it can only compare the presented biometric detail with an earlier measurement stored
on a data carrier or database,taking for granted the identity suggested by the accompanying identifying personal data.
While issuing an identity document or personal number or enrolling someone into a biometric system whether it is
inside or outside of the government, we unknowingly derive incorrect details fromsource documents, because we are
not able to verify the integrity or authenticity of the document and its true relation to the person presenting it. In many
cases, an information infrastructure designed to block an identity document or a personal number against use by
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others against the will of the rightful holder does not exist or may not be used. That is why identity fraud usually
succeeds, by way of simply accepting the suggested identity of a personal card, a personal number and, in the future,
that of a biometric template. This includes a cunningly cajoled admission to a sloppy fall back procedure that enables
the fraudster to casually present pass for somebody else. Often the resulting authorisation can no longer be seen
through during later phases of the process.
When checking someone's identity we spontaneously ask the question 'who are you', whereas it is usually sufficient to
know for sure that someone is the right person, regardless of who he is. Using a biometric characteristic, for example,
somebody can be accurately identified as the right person even if we cannot find out who he is. Carefully and
independently managed private and public pseudonyms such as personal numbers, pin codes, passwords, electronic
signatures or biometric details facilitate all sorts of accurate identity verifications. We often leave these opportunities
unused by this unnecessary emphasis on ‘identity’ that causes us to forget that anonymous and semi-anonymous
identity checks are also possible.
Unfortunately, these two attitudes, which are inadequate for detecting or combating identity fraud, are mutually
reinforcing. They explain why we spontaneously take identity measures that unintentionally increase rather than reduce
the opportunities to commit identity fraud. Tightening up the prevailing identity policy will play into the hands of the
identity fraudster. The most important consequence of the approach to the September 11 problemwill therefore probably
be that, despite all our good intentions, identity fraud will increase in scope and seriousness.
Combating identity fraud means a different way of thinking and also requires a turnaround in our approach to identity
checking. Our attention should shift fromthe identity document towards:
the person using it. The quality of the document is not unimportant, but not anymore the main issue in combating
identity fraud. A persons’ identity can also be checked by other personal details than those mentioned on the
identity document. These details can come from anywhere as long as they aren’t predictable or known. This can
legitimately and efficiently be done in an automated way following the approach, which I have described in my
publications about value chain computerisation (see ref 1). If we only have the personal details given in the identity
document at our disposal, how can we actually discover that somebody isn’t the legitimate holder of that identity
document? It is precisely those personal details that have enabled the identity fraudster to come up with a plausible
story in the first place;
the process of identity checking. Every situation of identity checking is different, depending on the context of the
checking process. In each situation, a successful identity fraud has a different social and economic value, identity
checking requires different personal details and chances to prevent identity fraud differ enormously. Thus, identity-
checking procedures are to be tailor-made. Predictable standard procedures undermine the effectiveness of any
identity checking process. The biggest leverage affect can be expected from elements of surprise built into a varied
systemof private and public identity verifications;
suitable checking details from independent sources that are not controlled by the person being checked or only
known to the rightful holder of the identity document, personal number or biometric detail;
the value of an identity instrument. Supra-chain use and mentioning of general personal numbers or biometric
details give an enormous social and economic value to poorly managed identity instruments that can be easily
misused by unauthorised persons. We must find ways to stop this creeping process of add ing more value to less
identity instruments unless counter-acted by chain-bound biometrics, tokens, personal numbers or PIN-codes and by
making use of other public bodies’ data.
5. AN OVERALL STRATEGY FOR IDENTITY FRAUD RESISTANT BIOMETRICS
Let us try, by way of conclusion, to formulate some headlines of an overall strategy for identity fraud resistant
biometrics, making use of the inherent variety of biometric systems and the two proposed perspectives on biometrics, the
value chain and the prevention of identity fraud.
1) A wide range of different biometric systems is possible and should give rise to tailor-made application of
biometrics. Twelve aspects of any biometric application imply choices that determine the effectiveness of the
application, its sensitivity to fraud and its consequences for personal safety and security. Biometrics will not realise
its full social potential until we recognise and utilise the wide-ranging possibilities offered by the anonymous or
semi-anonymous use of biometrics.
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2) A socially robust biometrics strategy should be based on the insight that biometric systems are basically chain -tied
and chain-specific, and that multi-chain use of a biometric system makes this supra-chain biometric system
unstable, difficult to manage and vulnerable to contamination and fraud. Supra-chain biometrics can be effective
and stable only if embedded in many different chain-bound and chain-specific control systems geared to its own
requirements, with inter-chain comparison of the results to detect identity fraud and to prevent the effects of
succesful identity fraud from spreading from one chain to another. Effective combating of identity fraud and misuse
of biometric images and templates will play an important role in keeping up our safety and privacy in an
information society.
3) Identity fraud as defined here, is forcing us to take a fresh look at biometric identity instruments and the use of
biometrics in the context of a specific process of identity checking, understanding the relevant legal culture. In our
western world, it generally turns out that identity measures and instruments that are useful in combating document
fraud do not provide solutions to combating identity fraud. Often they even have the reverse effect. Combating
identity fraud means a different way of thinking and also requires a turnaround in our approach to identity checking.
Our attention should shift fromthe identity document towards:
the person using it;
the process of identity checking;
suitable checking details;
the value of an identity instrument.
Identity checks will never be waterproof. Therefore one needs more than only one identity instrument, e.g. a biometric
detail. The use of other data can strengthen the prevention of identity fraud on condition that the person to be checked has
no control of its content and the procedure will not be predictable. Identity policy directed at combating identity fraud
doesn’t influence the need for better combating document fraud. It adds to identity policy an extra, new perspective and
different solutions. If identity instruments prove to be weakening the overall identity policy, then choices between
different solutions have to be made. This might be the case when a biometric detail will be given on identity documents
in such a way that an identity fraudster can read or use it. This is not to say that we should be reluctant to implement a
biometric system in places where it adds value in terms of protection of privacy or prevention of identity fraud. However,
we should be more aware of the barriers that keep us from benefiting from biometrics. Biometrics can be expected to
play an important role in an information society. Because the development of new technology usually goes through bad
patches, a well considered strategy is necessary for the application of biometrics. This strategy must be in place before
identity fraud is growing in number and negative effect, as we expect at the moment. Then biometrics will move t owards
its finest hour, but only if biometric data are reliable and biometrics confronts the identity fraudster with less predictable
verification processes and more risks of his identity fraud being spotted.
Thus, this contribution presents three major insights. Be aware of the fact that biometrics will only function well within
the boundaries of a value chain that cannot do without it to solve this particular chain’s dominant chain problem. Do not
opt for standardised large scale applications of biometrics for general use without countervailing measures, because it is
not possible to safeguard such a biometric application from identity theft, fraud and privacy infringements. Design the
use of biometrics to enhance verification processes with tailor-made solutions, instead of the current approach of
enhancing identity documents with a standardised solution.
REFERENCES
1. J.H.A.M. Grijpink, “Chain-computerisation for interorganisational policy implementation” and “Chain-
computerisation for better privacy protection”, in: Information Infrastructures & Policy 6 (1997-1999), IOS Press,
Amsterdam, March 2000.
2. J.H.A.M. Grijpink, “Personal numbers and identity fraud, number strategies for security and privacy in an
information society”, Part I en II, in: Computer Law and Security Report, vol. 18 (5 en 6) 2002, pp.327-332 en pp.
387-395, Elsevier Science Ltd, Oxford, UK , ISSN 02673649.
3. J.H.A.M. Grijpink, “Biometrics and Privacy”, in: Computer Law and Security Report, May/June 2001, vol. 17 (3)
2001, pp. 154-160, Elsevier Science Ltd, Oxford, UK, ISSN 02673649.
4. J.H.A.M. Grijpink, “Identity fraud as a challenge to the constitutionalstate”,in: Computer Law and Security Report,
January/February 2004, vol. 20 (1) 2004, Elsevier Science Ltd, Oxford, UK, ISSN 02673649.