The document discusses biometric smart cards that include fingerprint sensors and matching capabilities directly on the card. It provides an overview of biometric smart card technologies, including template-on-card, match-on-card, and biometric system-on-card approaches. The document also examines the anatomy and manufacturing process of biometric smart cards, highlighting components like the fingerprint sensor, microcontroller, RFID interface, and power supply considerations. Potential applications are identified in areas like identity verification, access control, financial services, and government programs.
The Rise of Biometric Smart Cards - Edition June 2018Antonio D'Albore
This is a public research on smart cards that embeds within their structure a fingerprint sensor. The paper explain their purpose, how they are made, whom is marketing them and a whole lot more.
The document discusses biometrics and fingerprint recognition technologies. It provides an overview of why biometrics are needed due to vulnerabilities in traditional identity methods like passwords. It then describes different biometric modalities like fingerprints, facial recognition, iris scanning, and compares their characteristics. The document explains how fingerprint recognition works from image capture to matching. It outlines fingerprint matching algorithms and challenges. Finally, it discusses applications of fingerprint biometrics and provides a brief history of biometrics.
This document discusses using biometrics for payment authentication. It introduces 3-D Secure protocols for cardholder authentication during e-commerce transactions. Sample biometric use cases are presented, including simple app login, step-up authentication for online transactions, and step-up authentication for account changes. The Visa ID Intelligence Biometrics service is described as a one-stop solution for biometric authentication that is FIDO Alliance certified. Out-of-band step-up authentication is proposed as a way to streamline 3-D Secure using biometrics on a mobile device.
Digital challenger banks are simplifying the financial world, creating a customer centric approach to services, and transforming the way banking is viewed by the public and the market
1. The document describes an IoT-based student attendance system that uses fingerprint recognition to identify students and automatically record attendance in real-time.
2. The system uses an ESP8266 microcontroller connected to WiFi to send student fingerprint IDs to a server when scanned. The server then updates attendance records and calculates percentages.
3. Students and teachers can check attendance and assignments in real-time through a mobile app, improving accuracy over traditional paper-based systems while saving time.
This document is a seminar report on biometrics technology submitted by Pavan Kumar M.T. to fulfill requirements for a Bachelor of Engineering degree. The report provides an introduction to biometrics, which uses human physiological or behavioral characteristics to identify individuals. It discusses the history of biometrics and provides a block diagram of typical biometrics systems. It also classifies and describes various biometric techniques including fingerprints, face recognition, hand geometry, iris recognition, speaker recognition, signature recognition, and gesture recognition.
INTERNET OF THINGS IN MEDICAL FIELD AND ITS APPLICATIONSArun balaji
The document discusses the Internet of Things (IoT) in the medical field and its applications. It covers topics such as IoT, ubiquitous computing, applications of IoT in healthcare like remote patient monitoring, and technological frameworks. IoT allows for collection and analysis of physiological data from sensors to improve healthcare access and quality while reducing costs. Challenges of IoT implementations include developing standards, networks, and analyzing sensor data. The future of IoT in healthcare depends on providing anytime, anywhere personalized care and monitoring over secure and reliable networks.
This document analyzes security issues with smart cards and their standards and implementations. It discusses known techniques for attacking smart cards to access secure information stored on them. The document aims to summarize existing security risks rather than report new vulnerabilities, in order to suggest hardware and software methods to prevent security leaks in smart card systems.
The Rise of Biometric Smart Cards - Edition June 2018Antonio D'Albore
This is a public research on smart cards that embeds within their structure a fingerprint sensor. The paper explain their purpose, how they are made, whom is marketing them and a whole lot more.
The document discusses biometrics and fingerprint recognition technologies. It provides an overview of why biometrics are needed due to vulnerabilities in traditional identity methods like passwords. It then describes different biometric modalities like fingerprints, facial recognition, iris scanning, and compares their characteristics. The document explains how fingerprint recognition works from image capture to matching. It outlines fingerprint matching algorithms and challenges. Finally, it discusses applications of fingerprint biometrics and provides a brief history of biometrics.
This document discusses using biometrics for payment authentication. It introduces 3-D Secure protocols for cardholder authentication during e-commerce transactions. Sample biometric use cases are presented, including simple app login, step-up authentication for online transactions, and step-up authentication for account changes. The Visa ID Intelligence Biometrics service is described as a one-stop solution for biometric authentication that is FIDO Alliance certified. Out-of-band step-up authentication is proposed as a way to streamline 3-D Secure using biometrics on a mobile device.
Digital challenger banks are simplifying the financial world, creating a customer centric approach to services, and transforming the way banking is viewed by the public and the market
1. The document describes an IoT-based student attendance system that uses fingerprint recognition to identify students and automatically record attendance in real-time.
2. The system uses an ESP8266 microcontroller connected to WiFi to send student fingerprint IDs to a server when scanned. The server then updates attendance records and calculates percentages.
3. Students and teachers can check attendance and assignments in real-time through a mobile app, improving accuracy over traditional paper-based systems while saving time.
This document is a seminar report on biometrics technology submitted by Pavan Kumar M.T. to fulfill requirements for a Bachelor of Engineering degree. The report provides an introduction to biometrics, which uses human physiological or behavioral characteristics to identify individuals. It discusses the history of biometrics and provides a block diagram of typical biometrics systems. It also classifies and describes various biometric techniques including fingerprints, face recognition, hand geometry, iris recognition, speaker recognition, signature recognition, and gesture recognition.
INTERNET OF THINGS IN MEDICAL FIELD AND ITS APPLICATIONSArun balaji
The document discusses the Internet of Things (IoT) in the medical field and its applications. It covers topics such as IoT, ubiquitous computing, applications of IoT in healthcare like remote patient monitoring, and technological frameworks. IoT allows for collection and analysis of physiological data from sensors to improve healthcare access and quality while reducing costs. Challenges of IoT implementations include developing standards, networks, and analyzing sensor data. The future of IoT in healthcare depends on providing anytime, anywhere personalized care and monitoring over secure and reliable networks.
This document analyzes security issues with smart cards and their standards and implementations. It discusses known techniques for attacking smart cards to access secure information stored on them. The document aims to summarize existing security risks rather than report new vulnerabilities, in order to suggest hardware and software methods to prevent security leaks in smart card systems.
This document discusses biometric security and its advantages over traditional password and PIN-based security methods. Biometrics provide increased security through unique physiological traits that cannot be easily guessed, shared, or stolen like passwords. Biometrics also increase convenience by eliminating the need to remember multiple passwords. Additionally, biometrics improve accountability by verifying user identity and activities more accurately than traditional methods. The document explores various biometric factors and how biometric systems work to authenticate users securely.
apidays LIVE London 2021 - From Open Banking to Embedded Finance by Simon Tor...apidays
apidays LIVE London 2021 - Reaching Maximum Potential in Banking & Insurance with API Mindset
October 27 & 28, 2021
From Open Banking, to Embedded Finance and Insurance
From Open Banking to Embedded Finance
Simon Torrance, Founder & CEO at Embedded Finance & Super App Strategies
This document provides an overview of the Internet of Things (IoT). It defines IoT as an ecosystem of connected physical objects that are accessible via the internet. These "things" can collect and transfer data without human intervention through built-in sensors. The document outlines several applications of IoT such as smart homes, wearables, connected cars, industrial uses, smart cities, agriculture, retail, energy, healthcare, and farming. It also discusses the rapid growth expected in IoT with billions of connected devices projected by 2020. However, the document notes issues like platform fragmentation, privacy/security concerns, and lack of interoperability that could impact adoption of IoT technologies.
Next IIoT wave: embedded digital twin for manufacturing IRS srl
Next IIoT wave will be a population of digital twin. A digital twin is a real-time digital replica of a physical device. Developing an embedded digital twin allows superior device diagnostic and failure anticipation. Discover how to to implement an embedded digital twin using real-time monitoring, physical models, and machine learning
AIA has developed an AI-powered online interview platform to make the agent recruiting process more flexible and efficient. The platform allows candidates to complete interviews anytime from any location. It uses AI to analyze interview responses and identify the best candidates objectively. This helps AIA standardize evaluations, expand interview volume, and track performance over the hiring process. The goal is to digitalize recruiting, reduce bias, and become the industry leader in AI-driven agency recruitment and training.
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.
Security for iot and cloud aug 25b 2017Ulf Mattsson
The document discusses security considerations for Internet of Things (IoT) and cloud computing. It notes that by 2020, IoT security needs will account for 2% of total IoT project costs. Supply chain security will account for 15% of IoT security spending. The document also discusses how 95% of cloud security failures will be the customer's fault. It recommends using tools like cloud access security brokers and data-centric audit and protection strategies to help secure data in cloud environments.
A fingerprint in its narrow sense is an impression left by the friction ridges of a human finger.Human fingerprints are detailed, unique, difficult to alter, and durable over the life of an individual making them suitable as long-term markers of human identity and may be employed by police or other authorities to identify individuals who wish to conceal their identity, or to identify people are incapacitated or deceased and thus unable to identify themselves, as in the aftermath of a natural disaster. When this fact is integrated for authentication purpose ,it creates an amazing lock system for Security purpose.Here we discuss what is Information Security ,how it is enhanced with the help of Fingerprint , its advantages, disadvantages.You will get a overall end to end scenario infront of you after going through all the slides. I have presented this topic in the Seminar presentation for completion of my M.Tech in Computer Science & Engineering.
This document discusses different types of biometrics used for automatic identification of individuals. It describes physiological biometrics such as fingerprints, facial recognition, iris scans, and retina scans. Behavioral biometrics include signature/handwriting and voice recognition. Multi-biometric systems integrate multiple biometrics to improve accuracy. Biometrics provide secure identification without documents but have disadvantages like expense and inapplicability to certain medical conditions. The document concludes that biometrics is a growing field that may replace passwords and keys.
Smart cards are credit card-sized cards with embedded microchips that can store up to 32,000 bytes of data. They come in two types - memory-only chips and microprocessor chips. Smart cards provide flexibility, security, and portability for applications like banking, mobile payments, ID verification, and access control. While smart cards offer advantages like increasing data storage and reliability, security remains a key disadvantage to address.
The document discusses the economic potential of generative AI. Some key points:
- Generative AI could add $2.6-$4.4 trillion annually to the global economy by automating tasks across various industries and business functions. This would increase AI's total economic impact by 15-40%.
- About 75% of the value from generative AI would come from use cases in customer operations, marketing/sales, software engineering, and research & development.
- All industry sectors would be significantly impacted, including banking, high tech, and life sciences. Banking alone could see $200-$340 billion in additional annual value from generative AI use cases.
This document describes an RFID system project that uses an Arduino Uno, ATmega 328p microcontroller, EM-18 RFID reader module, passive RFID tags, real-time clock, and 16x2 LCD. It explains the hardware and software components, including how the RFID reader detects tags and the microcontroller registers entries and exits to the LCD display. Key aspects of RFID technology like tags, readers, and middleware are also overviewed at a high level.
Smart cards are plastic cards with embedded integrated circuits that can be used for secure identification and financial transactions. They store data in their microchips and communicate with card readers using established protocols. The main types are contact cards that must be inserted directly into readers, contactless cards that communicate via radio frequency, and dual-interface cards that support both contact and contactless. Smart cards rely on operating systems and encryption to securely store and manage access to user data for applications such as ID cards, payments, and access control.
Smart cards can hold up to 32KB of data and have encryption capabilities. They provide security for banking, mobile payments, and ID verification. Smart cards use microprocessors and memory to store and process encrypted data for transactions and access control through card readers. They provide flexibility, security, portability and increasing storage capacity compared to magnetic stripe cards.
With latest technologies evolving like Machine Learning, QA's must know the right strategy to test applications as AI apps like Personal Assistants, Smart Cars have direct impact in our life.
How Insurers Can Harness Artificial IntelligenceCognizant
Once science fiction, artificial intelligence now holds vast potential for insurers interested in reinventing their business models and transforming customer experience.
Predictive Maintenance in the Industrial Internet of ThingsTibbo
Predictive maintenance uses sensors and remote monitoring to analyze equipment performance data over time. This allows maintenance to shift from a schedule-based to a condition-based model. By monitoring key performance indicators and detecting patterns in the data, predictive systems can estimate time to failure and remaining useful life. This enables repairs to be performed proactively before equipment actually fails.
This project aims at designing and developing biometric finger print technology based money transaction systems. As more global financial activity becomes digitally-based, banks are utilizing new technologies to develop next-generation identification controls to combat fraud, make transactions more secure, and enhance the customer experience.
A seminar on Smart Cards, its history, applications, working principle, architecture, classification and future scopes. Prepared for 3rd year ECE technical seminar.
The document discusses various applications of IoT in healthcare, including the OpenAPS open source artificial pancreas system, continuous glucose monitoring which tracks glucose levels, activity trackers used to monitor patients during cancer treatment, smart inhalers that detect inhaler use via Bluetooth, connected contact lenses that could measure glucose levels via tears, and a depression-fighting Apple Watch app that tracks mood over time. Some challenges of IoT in healthcare mentioned are device management, technical malfunctions, data security, active monitoring, and database management.
The Rise of Biometric Cards - Edition November 2017Antonio D'Albore
This document discusses biometric smart cards that include fingerprint sensors and processing capabilities. It provides an overview of biometric smart card technology, including potential applications, enrollment processes, security considerations, and the internal components and design of biometric smart cards. The core components include a fingerprint sensor, secure element for template storage and matching, microcontroller, and other electronics embedded in a standard smart card form factor. Various manufacturers are working to optimize the design and reduce costs to enable widespread adoption of biometric smart card technology.
This document discusses biometric security and its advantages over traditional password and PIN-based security methods. Biometrics provide increased security through unique physiological traits that cannot be easily guessed, shared, or stolen like passwords. Biometrics also increase convenience by eliminating the need to remember multiple passwords. Additionally, biometrics improve accountability by verifying user identity and activities more accurately than traditional methods. The document explores various biometric factors and how biometric systems work to authenticate users securely.
apidays LIVE London 2021 - From Open Banking to Embedded Finance by Simon Tor...apidays
apidays LIVE London 2021 - Reaching Maximum Potential in Banking & Insurance with API Mindset
October 27 & 28, 2021
From Open Banking, to Embedded Finance and Insurance
From Open Banking to Embedded Finance
Simon Torrance, Founder & CEO at Embedded Finance & Super App Strategies
This document provides an overview of the Internet of Things (IoT). It defines IoT as an ecosystem of connected physical objects that are accessible via the internet. These "things" can collect and transfer data without human intervention through built-in sensors. The document outlines several applications of IoT such as smart homes, wearables, connected cars, industrial uses, smart cities, agriculture, retail, energy, healthcare, and farming. It also discusses the rapid growth expected in IoT with billions of connected devices projected by 2020. However, the document notes issues like platform fragmentation, privacy/security concerns, and lack of interoperability that could impact adoption of IoT technologies.
Next IIoT wave: embedded digital twin for manufacturing IRS srl
Next IIoT wave will be a population of digital twin. A digital twin is a real-time digital replica of a physical device. Developing an embedded digital twin allows superior device diagnostic and failure anticipation. Discover how to to implement an embedded digital twin using real-time monitoring, physical models, and machine learning
AIA has developed an AI-powered online interview platform to make the agent recruiting process more flexible and efficient. The platform allows candidates to complete interviews anytime from any location. It uses AI to analyze interview responses and identify the best candidates objectively. This helps AIA standardize evaluations, expand interview volume, and track performance over the hiring process. The goal is to digitalize recruiting, reduce bias, and become the industry leader in AI-driven agency recruitment and training.
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.
Security for iot and cloud aug 25b 2017Ulf Mattsson
The document discusses security considerations for Internet of Things (IoT) and cloud computing. It notes that by 2020, IoT security needs will account for 2% of total IoT project costs. Supply chain security will account for 15% of IoT security spending. The document also discusses how 95% of cloud security failures will be the customer's fault. It recommends using tools like cloud access security brokers and data-centric audit and protection strategies to help secure data in cloud environments.
A fingerprint in its narrow sense is an impression left by the friction ridges of a human finger.Human fingerprints are detailed, unique, difficult to alter, and durable over the life of an individual making them suitable as long-term markers of human identity and may be employed by police or other authorities to identify individuals who wish to conceal their identity, or to identify people are incapacitated or deceased and thus unable to identify themselves, as in the aftermath of a natural disaster. When this fact is integrated for authentication purpose ,it creates an amazing lock system for Security purpose.Here we discuss what is Information Security ,how it is enhanced with the help of Fingerprint , its advantages, disadvantages.You will get a overall end to end scenario infront of you after going through all the slides. I have presented this topic in the Seminar presentation for completion of my M.Tech in Computer Science & Engineering.
This document discusses different types of biometrics used for automatic identification of individuals. It describes physiological biometrics such as fingerprints, facial recognition, iris scans, and retina scans. Behavioral biometrics include signature/handwriting and voice recognition. Multi-biometric systems integrate multiple biometrics to improve accuracy. Biometrics provide secure identification without documents but have disadvantages like expense and inapplicability to certain medical conditions. The document concludes that biometrics is a growing field that may replace passwords and keys.
Smart cards are credit card-sized cards with embedded microchips that can store up to 32,000 bytes of data. They come in two types - memory-only chips and microprocessor chips. Smart cards provide flexibility, security, and portability for applications like banking, mobile payments, ID verification, and access control. While smart cards offer advantages like increasing data storage and reliability, security remains a key disadvantage to address.
The document discusses the economic potential of generative AI. Some key points:
- Generative AI could add $2.6-$4.4 trillion annually to the global economy by automating tasks across various industries and business functions. This would increase AI's total economic impact by 15-40%.
- About 75% of the value from generative AI would come from use cases in customer operations, marketing/sales, software engineering, and research & development.
- All industry sectors would be significantly impacted, including banking, high tech, and life sciences. Banking alone could see $200-$340 billion in additional annual value from generative AI use cases.
This document describes an RFID system project that uses an Arduino Uno, ATmega 328p microcontroller, EM-18 RFID reader module, passive RFID tags, real-time clock, and 16x2 LCD. It explains the hardware and software components, including how the RFID reader detects tags and the microcontroller registers entries and exits to the LCD display. Key aspects of RFID technology like tags, readers, and middleware are also overviewed at a high level.
Smart cards are plastic cards with embedded integrated circuits that can be used for secure identification and financial transactions. They store data in their microchips and communicate with card readers using established protocols. The main types are contact cards that must be inserted directly into readers, contactless cards that communicate via radio frequency, and dual-interface cards that support both contact and contactless. Smart cards rely on operating systems and encryption to securely store and manage access to user data for applications such as ID cards, payments, and access control.
Smart cards can hold up to 32KB of data and have encryption capabilities. They provide security for banking, mobile payments, and ID verification. Smart cards use microprocessors and memory to store and process encrypted data for transactions and access control through card readers. They provide flexibility, security, portability and increasing storage capacity compared to magnetic stripe cards.
With latest technologies evolving like Machine Learning, QA's must know the right strategy to test applications as AI apps like Personal Assistants, Smart Cars have direct impact in our life.
How Insurers Can Harness Artificial IntelligenceCognizant
Once science fiction, artificial intelligence now holds vast potential for insurers interested in reinventing their business models and transforming customer experience.
Predictive Maintenance in the Industrial Internet of ThingsTibbo
Predictive maintenance uses sensors and remote monitoring to analyze equipment performance data over time. This allows maintenance to shift from a schedule-based to a condition-based model. By monitoring key performance indicators and detecting patterns in the data, predictive systems can estimate time to failure and remaining useful life. This enables repairs to be performed proactively before equipment actually fails.
This project aims at designing and developing biometric finger print technology based money transaction systems. As more global financial activity becomes digitally-based, banks are utilizing new technologies to develop next-generation identification controls to combat fraud, make transactions more secure, and enhance the customer experience.
A seminar on Smart Cards, its history, applications, working principle, architecture, classification and future scopes. Prepared for 3rd year ECE technical seminar.
The document discusses various applications of IoT in healthcare, including the OpenAPS open source artificial pancreas system, continuous glucose monitoring which tracks glucose levels, activity trackers used to monitor patients during cancer treatment, smart inhalers that detect inhaler use via Bluetooth, connected contact lenses that could measure glucose levels via tears, and a depression-fighting Apple Watch app that tracks mood over time. Some challenges of IoT in healthcare mentioned are device management, technical malfunctions, data security, active monitoring, and database management.
The Rise of Biometric Cards - Edition November 2017Antonio D'Albore
This document discusses biometric smart cards that include fingerprint sensors and processing capabilities. It provides an overview of biometric smart card technology, including potential applications, enrollment processes, security considerations, and the internal components and design of biometric smart cards. The core components include a fingerprint sensor, secure element for template storage and matching, microcontroller, and other electronics embedded in a standard smart card form factor. Various manufacturers are working to optimize the design and reduce costs to enable widespread adoption of biometric smart card technology.
Heterointegration Coming of Age in 2015 - MEF 2015Paul Werbaneth
Heterogeneous integration, which involves integrating different device technologies such as CMOS and MEMS onto a single chip, is coming of age commercially. This integration allows for more sophisticated capabilities in smaller form factors by enabling multiple sensors, signal processing, and telemetry capabilities. Commercial examples of heterogeneous integration discussed in the document include Sony's stacked CMOS image sensors using TSVs, SiTime's MEMS oscillators integrated using epi-poly sealing, and various MEMS startups developing novel integration approaches like mCube's monolithic MEMS-on-CMOS using through-silicon vias. The document examines the definition and various approaches to heterogeneous integration in the microelectronics industry.
The Rise of Biometric Cards - Edition January 2018Antonio D'Albore
This is an independent research on the subject of Biometric Smart Cards.
Edited by Antonio D'Albore and Jean-François Mainguet.
Full work with extended comments on www.embeddedsecuritynews.com.
The Rise of Biometric Cards - Edition February 2018Antonio D'Albore
This document discusses biometric smart cards that contain fingerprint sensors. It provides a history of fingerprint sensors in phones and an overview of what a biometric smart card is. It then covers potential applications of biometric smart cards, including financial, government, healthcare and access control uses. The document examines important considerations for biometric smart card design and manufacturing, such as the enrollment process, card anatomy, and lamination techniques. It also outlines some real-life problems that biometric smart cards could help address.
Improvement of a PIN-Entry Method Resilient to ShoulderSurfing and Recording ...IJRTEMJOURNAL
The scope of this work extends to system components (for example service providers, networks,
servers, hosts, applications, processes and personnel) which are used to exchange PIN-related data. The PIN
Guidelines in this document encompass PIN security within any one system or sub-system and between systems.
This process designs 10 digit keypad with random RGB color SCHEME using a Fast Finite-State Algorithm for
Generating RGB Palettes of Color. In this work, we propose a color finite-state LBG (CFSLBG) algorithm that
reduces the computation time by exploiting the correlations of palette entries between the current and previous
iterations.
IRJET - Precocious ATM System using Iris ScannerIRJET Journal
This document proposes a new ATM system with enhanced security features using iris scanning and fingerprint scanning technologies. The system uses an iris scanner as the primary authentication method along with a fingerprint sensor embedded in the ATM card as a secondary authentication. It aims to address the increasing problem of ATM skimming and fraud. The system architecture includes a Raspberry Pi microcontroller, RFID module to access customer accounts, fingerprint sensor for authentication, iris scanner, and a database containing user iris and fingerprint data. The iris scanning provides strong authentication since iris patterns do not change over a person's lifetime. The addition of fingerprint scanning on the ATM card adds another layer of security by requiring both biometrics to authenticate transactions. The proposed system is expected
This document provides an overview and highlights of IDEX Biometrics for Q4 2014 and the full year 2014. It discusses IDEX's product development progress, including new partnerships and technology demonstrations. It outlines IDEX's strategy to target the mobile, internet of things, and payment card markets, which it estimates to be multi-billion dollar opportunities. It also provides an outlook for 2015, anticipating key product launch and partnership milestones that will drive the company's growth.
Disruptive Trends Fueled by AI & Camera Edge AnalyticsMemoori
This document discusses disruptive trends in artificial intelligence (AI) and camera edge analytics. It provides an overview of Security & Safety Things, a Bosch startup, and their open platform and application store for AI video analytics apps. Key points discussed include the benefits of edge computing over cloud-based analytics, how open marketplaces allow for new use cases and flexible installations. Several smart building and smart city use cases enabled by AI and edge computing are presented, such as automated traffic management, pattern recognition, and data sharing between vehicles.
Synaptics’ Under-Display Fingerprint Scanner Inside the VIVO X21 UD Smartphonesystem_plus
Optical fingerprint technology allows integration behind the display.
More information on that report at: http://www.systemplus.fr/reverse-costing-reports/synaptics-under-display-fingerprint-scanner-inside-the-vivo-x21-ud-smartphone/
The document is an industrial training report submitted by Ankit Kumar to fulfill requirements for a Bachelor of Technology degree in Computer Science and Engineering. The report discusses artificial intelligence in semiconductors and its impacts. Key points include how AI will affect semiconductor design through architectural improvements to address massive data processing needs in AI. AI also provides opportunities for the semiconductor industry through growing demand for AI chips used in applications like autonomous vehicles, robotics, and IoT devices. Challenges for the semiconductor industry include meeting rising demand for specialized AI chips while managing costs and developing new software ecosystems for industry partners.
IRJET- Design and Implementation of Fingerprint based Bank Locker System usin...IRJET Journal
This document describes the design and implementation of a fingerprint-based bank locker system using an ARM7 microcontroller and GSM technology. The system uses a fingerprint sensor module to store fingerprints of authorized users. When a fingerprint is detected that matches an authorized user, an ARM7 microcontroller connected to a GSM module will send an authentication SMS to the user. If authenticated, the microcontroller will activate a motor to automatically open the bank locker door, providing secure access. The system aims to provide stronger security compared to traditional bank lockers that use keys, as fingerprints are more reliable identifiers than keys that can be lost or stolen.
The emerging global trends in IoT are discussed in this presentation. We also look at various other trends in IoT which is now a very common factor that is being used all over the globe
The growth on the Internet of Things (IoT) has been astonishing.
From Home to Cities,
Wearables to Driverless Cars, Agriculture to Factories ,
more and more things are being connected and embedded with technology and generating big-data.
- When Data became really ‘Big-Data’?
- What are the characteristics/complexities of Big-Data in IoT?--
- What is the role of Analytics and How is it different in IoT context?
- What are the use-cases of IoT with Analytics in Action?
- When AI meets IoT: Frameworks, Tools, Technologies
Hashtech Systems Pvt Ltd is a turn-key provider of kiosk solutions. We design and manufacture both hardware and software of the products. We have a world-wide list of customers for our kiosk products.
IRJET- Secure Automated Teller Machine (ATM) by Image ProcessingIRJET Journal
1) The document discusses using image processing techniques like fingerprint scanning, iris scanning, and face recognition for secure authentication in automated teller machines (ATMs).
2) It analyzes different biometric methods and proposes two algorithms that use biometrics like fingerprints for verification to prevent fraudulent ATM access and reduce attacks on the system.
3) The algorithms are meant to provide a higher level of security compared to existing ATM systems by making the ATM only accessible based on biometric matching, though implementing such a system comes at a higher cost.
IP-based video surveillance has grown rapidly but competition is increasing as more firms enter the market. While some companies have maintained margins by continually improving technology, price competition will likely set in as technologies mature and reach sufficient quality for most applications. As features overshoot customer needs and it becomes difficult to differentiate, firms will have to compete on price, triggering a potential price war. The timing remains uncertain but price competition appears inevitable as the industry follows patterns seen in other digital technologies.
Edge AI Smart Manufacturing - Defect Detection and Beyond (GTC 2019)Wei-Chao Chen
These are the slides about a talk I gave at NVIDIA's GTC 2019 (GPU Technology Conference) at San Jose, CA.
====
https://gputechconf2019.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=263092
We'll discuss our experiences working with first-tier electronics manufacturing facilities to apply machine learning techniques to their product design processes and production pipelines. We'll provide details about several applications in the domains of circuit defect and product appearance inspections, quality control, and the associated edge AI algorithms, and explain the machinery we created to solve these problems. When applying AI to smart manufacturing, the first problems that come to mind are usually automatic product inspection, but in reality we often need to carefully redefine the problems to yield a satisfactory solution and return on investment.
The Internet of Things - beyond the hype and towards ROIPerry Lea
How do you move beyond the hype of IoT and towards profitability? This short lecture examines the hype and origin of IoT and the reality of the industry. It then talks about my experiences with the industry, customers, and technologists. Some have outright failed in IoT projects, others are succeeding.
Get beyond the prototype and lab experiment.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
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.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
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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 .
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
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Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
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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.
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FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
The Rise of Biometric Cards
1. The Rise of Biometric Cards
state of the art and future challenges for card manufacturers
Antonio D’Albore
Editor-In-Chief
Embedded Security News
5-6 October 2017
Milan - Italy
2. Agenda
• Fingerprint Consumer Acceptance Timeline
• What is a Biometric Smart Card?
• Template-On-Card
• Match-On-Card
• Biometric System-on-Card
• Biometric Card Applications
• Anatomy of a BSC
• Fingerprint Card Key Manufacturing Steps
• Fingerprint Card Personalization
• Fingerprint Cards Value Chain
• Key Trials / Pilots
• Key Companies
• The Offer
• Global BSC Shipment Forecast Y2017-2020
• Global BSC ASP Forecast Y2017-2020
• Normative References
• Fingerprint Cards Acceptance Factors
• Fingerprint Cards Potential Blocking/Risk Factors
• The Future
• Q&A
5-6 October 2017
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 2
3. Fingerprint Consumer Acceptance Timeline
Siemens and
Triodata develop
phone prototype
with fingerprint
sensor.
1998 2000
Sagem launch MC
959 ID, phone with
STM fingerprint
sensor
2011
Apple file Patent
“Devices and
Methods for
Providing Access to
Internal
Components”
(US20120258773A1)
2012
Apple buys
AuthenTec for
$356M
2013
Apple launch iPhone
5S
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 3
4. What is a Biometric Smart Card?
A Biometric Smart Card (BSC), Fingerprint Card or
Biometric System-on-Card (BSoC) is a Smart Card where:
• Biometric sample capture (enrolment /
verification)
• Minutiae Template extraction
• Template storage
• Templates matching
is performed within the card.
A BSoC shall never transfer any biometric sample and biometric reference data to any external terminal.
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 4
7. Enrolment
Fingerprint
acquisition
In-Card
template
creation
Template stored in
Secure Element
or Secure Flash
Matching
Fingerprint
acquisition
In-Card
template
creation
Match result
In-Card
templates
matching
Biometric-System-on-Card (BSoC) (1/2)
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 7
8. Extract
Measurement
Query
Template
Biometric-System-on-Card (BSoC) (2/2)
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 8
Capture
Pre-
Processing
Feature
Extraction
Template
Creation
Reference
template
Compare
Matching
Adaptive
Score
decision
Authenticate
Enrolment
Authentication
9. Biometric Card Applications
• Identity Verification
• Proof of life
• Government Ration/Subsidy Card
• Driving License
• Loyalty / Membership
• Secure Access
• Door entry
• System Login
• Online transaction
• Online Gaming
• Online Voting
• Healthcare & Insurance
• Government Healthcare cards
• Health Insurance Cards
• Financial
• Debit / Credit Cards
• eCommerce
Enabler of three-factors
(3FA) authentication
Something I have
CARD
Something I know
PIN
Something I am
FINGERPRINT
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 9
10. Anatomy of a Biometric Smart Card (BSC)
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 10
11. Back Side Overlay
Anatomy of a BSC: the components
Back Side Layer
Inlay
Front Side Layer
Front Side Overlay
Micro module
Fingerprint sensor
Sensor Bezel
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 11
12. Anatomy of a BSC: Card Body
Card body
Construction
- Industry’s choice for mass production
- Use existing production process
- Additional step for FPS cavity making
Hot Lamination
Non-Moulded
card body
Cold Lamination
Moulded
card body
Non-Moulded
card body
- Ok for prototyping / small volumes
- Can be manual labour intensive
- Popular in Taiwan / South Korea
- Not necessarily cheaper than HL
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 12
13. Anatomy of a BSC: The Inlay
SE Contacts Pad
Antenna
Status LEDs
FPS Contacts Pad
Display Driver IC
Display
Battery / SC
Buzzer
Power Button
Passive Components
MCU / ASIC
RF IC
FPS Controller IC
Secure FlashFlexible PCB
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 13
Inlay size is 1 – 2 mm (0.04 - 0.07 in) smaller than card W / H
Some BSC Integrators deliver inlays with FPS already embedded
14. Anatomy of a BSC: Operative Strategies
Card body
Construction
IC Power Supply
Power is supplied to SE only after successful fingerprint
verification. No successful verification = no ATR.
Card body
Construction
APP Status
The terminal check APP status. Default APP state = Disabled.
After successful fingerprint verification APP state = Enabled.
Card body
Construction
ATR ATR
Double ATR
Terminal checks for a 2° ATR to occur within a predefined
number of clock cycles. If Fingerprint verification after the first
ATR fails, then the second ATR is not sent.
5-6 October 2017
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 14
15. Anatomy of a BSC: Power Supply (1/2)
With Replaceable Battery
With Rechargeable Battery
+
Energy Harvesting
With Rechargeable Battery
+
Chip Contact
With Non Rechargeable Battery
With Battery
No Battery
+
Energy Harvesting
No Battery
+
Chip Contact
Without Battery
No Battery
+
Supercapacitor (SC)
In some contexts SC are considered
Rechargeable Batteries
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 15
16. Anatomy of a BSC: Power Supply (2/2)
• Flat, printed Lithium Ion batteries are easy to implement but can create issues:
• Card heating
• Transport issues (Air, Courier)
• In some countries, treated as “Dangerous Goods”
• In some countries, subject to certification
• Supercapacitors (SC) often used in Energy Harvesting BSC for short-term energy
storage.
• BSC without battery requires card reader / NFC reader for enrolment.
• Replaceable batteries used in Non-ISO card (thickness) for Access Control applications.
5-6 October 2017
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 16
17. Anatomy of a BSC: FPS Cavity Making
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 17
Suitable for mass
production
Require modification to chip
cavity milling equipment
Mechanical Milling
Most popular method for
cavity making
Color change issue with PVC
/ PETG
Good results on PC
Technique under
development
Laser Milling
Very precise
Smooth and clean cavity
No debris removal
Injection Moulding
Complex industrialization
for mass production
Chip and FPS sensor cavity
are created during injection
Cavities punched out from
PVC sheet
Operation performed after
sheet printing
Sheet Punching
Requires precise alignment
of lamination sandwich
Suitable for mass
production
18. Anatomy of a BSC: Swipe vs Area Sensors
Generate large Image
Problematic torsion/bending test
Swipe
Sensors
Unreliable in many applications
Significant learning curveSmall footprint
Easy bonding to FPCB
Area
Sensors
Accurate Subject to scratches
Large footprintEasy to use
Flexible are ideal for smart cards
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 18
19. Anatomy of a BSC: Key Sensor Makers
Elan Microelectronics Fingerprint Cards Flexenable
IDEX Infinity Electronic Biotech Innolux Next Biometrics
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 19
Datang Microelectronics
20. Anatomy of a BSC: Area Sensors
• Average thickness ~ 0,3 mm (0.011 in)
• Full fingerprint scan ~ 1 sec
• 1.8 ÷ 3.3 V operations
• Resolution ≥ 500 dpi (sensors with lower resolution compensate with larger active area)
• Output 8 bit, 256 grey scale values
• Wakeup upon touch (few ms wait)
• Read fingerprint from any angle
• Protective coating to increase durability
• ESD Protection ±15 kV (IEC 61000-4-2)
• Durability ≥ 1 M touches
• Some models include Bezel into their design
• For mass production shall be delivered in Super 35 mm Tape format
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 20
21. Anatomy of a BSC: Bezel
Cover sensor bonding pads
Aesthetic
Cover sensor cavity walls
Bezel, when present, have Operative and Aesthetic functions:
Must be flexible enough to comply with dynamic torsional stress of ISO/IEC 10373-1
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 21
Electrically drive the fingertip during the
sensing process
Help keep sensor in place
Operative
ESD protection
Protect sensor from environment
*
*For bezel not embedded into sensor design
22. Anatomy of a BSC: Cross Section
Card bodyFingerprint Area Sensor
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 22
Gap between card front surface and sensor active
surface:
• Reduce FPS wear & tear
• Avoid contact with plate during lamination
For sensors mounted on Inlay before lamination
*
*
FPS Bezel
Bezel should not
extend over card
surface to avoid
scratches
23. Anatomy of a BSC: MCU / ASIC
• ARM Cortex M4, 32bit RISC de facto standard as general purpose MCU
• Perform Template Match when this is not done by SE
• Coordinate/control BSC components
• Most BSC makers are designing their own ASIC:
• Integrate FPS IC + MCU + Flash + RF IC.
• Cheaper when manufactured in volume.
• Easier to integrate (less components).
• Will drive inlay cost down.
• Can be done only when product with generic MCU
reach maturity.
• Time-to-Market 18-24 months.
5-6 October 2017
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 23
24. Anatomy of a BSC: RF IC
• The RF IC perform multiple tasks:
• Harvest power from RFID excitation field
• Voltage Regulator
• Clock Generator / Clock Regulator
• Reset Generator
• Anti-collision mechanism
• Distribute power supply to both SE + BSC MCU/ASIC + FPS
• When present, charge Rechargeable Battery or
Supercapacitor
5-6 October 2017
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 24
25. Anatomy of a BSC: Led / Buzzer / LCD
Other elements that can be present on a Biometric Card:
LED
Buzzer
Flexible LCD / e-paper
Ensure compliance with ISO/IEC 17839-1:2014 - Biometric System-on-Card --
Part 1: Core requirements
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 25
26. BSC Contactless Interfaces / Transponders ICs
ISO 14443 Type A, B, C
NFC
Bluetooth
HID 125 kHz / HID iClass
Mifare Classic / DESFire EV1
Legic Advant
Atmel 5577
Popular on BSoC Access Cards
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 26
27. Anatomy of a Fingerprint Card: Making of FPCB
FPCB
Copper Plating
Laser Drill
Line Formatting
AOI & PSR
Gold Plating
Marking
SMD / COF
Solder Paste
Mount
Reflow
Prelam / Packaging
Placement on Template
Inlays Packing
Sensor mount on Inlay
Sheets Packing
Prelam sheets making
Inlays Packing
*
*
Delivery also possible as prelam Sheets
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 27
Flexible PCB often made of polyimide
Bonding
28. Single steps when bezel is integrated into FPS design
Fingerprint Card Key Manufacturing Steps
Sheets Printing
Sheet FPS Cavity
Punching
Layers Collation
Sheets
Lamination
Sheet Punching
Cards Gathering
Micromodule
Cavity Milling
Micromodule
Embedding
FPS Cavity Milling
Anisotropic
conductive epoxy
on contact pads
cavity walls
coated with
adhesive epoxy
FPS Embedding Bezel Placement
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 28
FPS embedded
after lamination
FPS embedded
before lamination
29. Fingerprint Card Personalization
EmbossLaser engraving Direct-To-Card
Overlaminate
Overlay
Sensor bezel / card thickness can potentially create interference / jam in personalization equipment
Inkjet
Rear Indent
Front Indent
5-6 October 2017
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 29
Retransfer
30. Fingerprint Cards Value Chain
BIOMETRIC CARD
SOLUTION
PROVIDER
IPs
The Biometric Card Solution Provider can design Biometric Engine, ASIC and Inlay in house.
Sensor
Manufacturer
ASIC
Foundry
Sensor
Designer
Inlay
Manufacturer
ASIC
Designer
Biometric
Engine Designer
MCU / RF ICs
Supplier
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 30
Inlays / Prelams
Sensors
APIs
Dev. Tools
Test Tools
Enrolment SW
Delivery
Card
Manufacturer
IP Licenses
31. Key Trials / Pilots
2014
Sparebanken DIN
Norway
Zwipe + MasterCard
Payment 2017
Pick n Pay
South Africa
OT-Morpho + MasterCard
2017
Absa Bank
South Africa
OT-Morpho + MasterCard
2016
Bundesdruckerei
Germany
Physical Access
2015
Danske Bank
Denmark
Zwipe + MasterCard
2017
Woori Bank
South Korea
Korea Smart ID
Logical Access
2017
Instanbul Municipality
Turkey
Korea Smart ID
Applications
2017
Bulbank Bank
Bulgaria
OT-Morpho + MasterCard
2015
Battistolli
Italy
Card-Tech + IngenicoIdentification
5-6 October 2017
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The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 31
2017
Pleinair Casino
France
MeReal Biometrics
2017
United Nations
Switzerland
KSID + Elan + Jinco
2017
AirPlus
Germany
Kona I + FPC
32. Key Companies: CardLab ApS
2003
Founded
Denmark
HQ
Private
Type
Frank Sandeløv
CEO
• Strong IP Portfolio focusing on Powered Smart Cards
• Partnership with QuardLock for development of a OTP BSoC
• 50K€ funding by EU under Horizon 2020 project
• Own development of minutiae algorithm
• Trials in USA and UAE
• Hot laminated products under industrialisation (Thailand), expected launch Q1/Y2018
• Key Products:
• Non-rechargeable battery dual interface BSC inlay and cards
• Non-rechargeable battery contactless NFC BSC inlay and cards
• Inlays with multiple items: fps + display + dynamic magstripe + led
Cardlab.com
WEB
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33. Key Companies: Card-Tech S.r.l.
2006
Founded
Italy
HQ
Private
Type
Fabrizio Borracci
CEO
• PinKey Smart, BSoC with swipe sensors, launched Cartes Y2014
• Holds patents on In-Card biometric verification
• Manufacturing partnership with top-tier card vendor
• Partnership with IDEX for sensors, sensor embedded on inlay
• No bezel
• Template Matching on smart card chip
• Key Products:
• Contact BSC inlay
• Dual Interface Energy Harvesting BSC inlay (under development)
card-tech.it
WEB
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34. Key Companies: Datang Microelectronics
1996
Founded
China
HQ
Private
Type
Yanhui Yang (杨延辉)
CEO
• Belonging to Datang Telecom Technology, a State owned 3.3B$ industrial group
• China largest design house + foundry for Smart Card ICs
• In-house development + production of Capacitive Area and Swipe sensors
• In-house development + production of MCU + RF Chip
• In-House development + production of flexible BSC inlays
• China strongest company in smart card-related development
• Key Products:
• Contact BSC with rechargeable battery
• Dual Interface BSC with rechargeable battery
• Access Control BSC (rechargeable battery)
dtm.com.cn
WEB
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35. Key Companies: Jinco Universal Co. Ltd.
HQ Type CEO
• Asia’s strongest OEM/ODM of customized powered smart cards / inlays
• Partnership with Korea Smart ID (KSID) for Card Production
• Partnership with Elan Microelectronics for sensors
• Strong promotion in South Korea / China / Taiwan / South East Asia
• Specific patents on BSoC
• Working on retina recognition smart card (patented)
• Key Products:
• Contactless BSC with rechargeable battery
• Dual Interface, energy harvesting BSC
• Dual Interface, rechargeable battery BSC with dynamic magstripe and display
WEB
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Founded
2005 Taiwan Private Ted Lin (林武旭) Jinco.com.tw
36. Key Companies: Mereal Biometrics Ltd.
2009
Founded
Hong Kong
HQ
Private
Type
Philippe Blot
CEO
• Investment by French Groupe Partouche (Casino, Hotel, Events)
• Co-founded by Philippe Blot (UINT CEO)
• Patented Acoustic / RFID OTP transmission to terminal
• Sales & Marketing base in Hong Kong
• UINT Limoges (France) engineering & manufacturing plant
• Current production capacity ~250K cards / month
• Key Products:
• Dual Interface, rechargeable battery BSC with Swipe Sensor
• Dual Interface, rechargeable battery BSC with Area Sensor from FPC
• Contactless Portable Charger
merealbiometrics.com
WEB
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37. Key Companies: MoriX Co., Ltd.
2005
Founded
Japan
HQ
Private
Type
Morihiko Kubota
(久保田守彦)
CEO
• Partnership with Toshiba Solutions Corp.
• Selling Cards & Fingerprint Sensors
• In-house Fingerprint Matching algorithm development
• COF Sensor Co-developed with ASD: 508 dpi, 9.4 x 11 mm active area
• NFC BSC under development
• Key Products:
• BSC contact with non-rechargeable battery
• BSC contact with display and non-rechargeable battery
• Fingerprint Sensor module
morix-ic.com
WEB
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38. Key Companies: Tactilis Pte
2014
Founded
Singapore
HQ
Private
Type
Michael Gardiner
CEO
• Firm founded by industry seasoned professionals
• Privately funded
• Exclusive, patented manufacturing process
• Fully equipped plant in Penang, Malaysia
• Offering BSC Inlays & complete cards
• Supporting a tier-one card vendor
• Integrate secure flash up to 4GB
• Low profile communication strategy
• Key Products:
• Dual interface, energy harvesting, BSC with large area sensor card + inlay
tactilisltd.com
WEB
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39. Key Companies: Zwipe AS
2009
Founded
Norway
HQ
Private
Type
Kim Kristian Humborstad
CEO
• Strongest IP Portfolio for Biometric solutions with >50 patents
• Kuang-Chi Group partner for China JV and 20.1% stake
• First company to have trials with MasterCard
• Inlay-based solution tailored for card manufacturers
• Sensor agnostic solution, currently delivering FPC FPS
• Ongoing development of custom ASIC to reduce components + costs
• Key Products:
• Access Control BSC (non-ISO, replaceable battery)
• Contactless Energy harvesting BSC (ID)
• Dual Interface Energy Harvesting BSC inlay (Payment)
zwipe.no
WEB
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40. The Offer: Whom is Offering What
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Non-
Rechargeable
Battery
Rechargeable
Battery
+
Chip Contact
Rechargeable
Battery
+
Energy
Harvesting
No Battery
+
Chip Contact
No Battery
+
Energy
Harvesting
Replaceable
Battery
CardLab
Card Tech
Datang Microelectronics
Jinco Universal
MeReal Biometrics
Morix
Tactilis
Zwipe
41. The Offer: Tier 1 Company Developments
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Gemalto
•Ongoing
Industrialization in
France & Germany
R&D / plants
•Marketing activity
started
•Expected Launch
Q1/2018 Idemia
•First among top card
vendors to have
completed
industrialization
•BSC in production
•Leverage Morpho
expertise in
biometric
•Active joint
promotion with
MasterCard
•In-house inlay
production, no OEM
sell
•Current solution with
non-rechargeable
battery
G+D
•Not aware of any
development
•Could continue
development of
Bundesdruckerei
GoID! fibre
composite card if
migrated to Veridos
portfolio
KoreaSmartID(KSID)
•So far, KSID is the
company with the
largest quantity of
Biometric Cards
produced
•Key Customers:
•UN
•Istanbul
Municipality
•Woori Bank
42. Global BSC Shipment Forecast Y2017-2020
0
100
200
300
400
2017 2018 2019 2020
18
90
250
400Millionofcards
Source: ABI Research, Fingerprint Cards, Gartner, HIS, Nilson Report, Precise Biometrics, Statista, Carnegie Research, Strategy Analytics, Swedbank, Antonio D’Albore estimates.
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43. Global BSC ASP Forecast Y2017-2020 (K pieces)
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$-
$2
$4
$6
$8
$10
$12
$14
$16
$18
$20
2017 2018 2019 2020
Clamshell BSC
Battery BSC
No battery BSC
44. Normative References
Standards developed by Technical Committee ISO/IEC JTC 1/SC 17
Cards and personal identification
The current area of work for JTC 1/SC 17 consists of:
• Identification and related documents
• Cards, and
• Security devices associated with their use in inter-industry
applications and international interchange
Details at https://www.iso.org/committee/45144.html
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45. Normative References
Type S1 “conventional”
Dimensions ID-1 as ISO/IEC 7810
Torsion and bending as ISO/IEC 7816-1
Contact interface as specified in ISO/IEC 7816-3
USB interface as specified in ISO/IEC 7816-12
Contactless interface as specified in ISO/IEC 14443
Type S2 (ID-T) “ticker” – NEW FORMAT
Card is 2.5 mm (0.098 in) thick
2.5 mm thick card cannot be inserted in card slot by
mistake
Width / height as an ISO/IEC 7810 card
Need not conform to ISO/IEC 7816-1 flexibility
Supports only ISO/IEC 14443 contactless interface
Regardless of S1 or S2, a BSC must have some form of feedback mechanism such as LCD display, LED or buzzer
ISO/IEC 17839-1:2014 - Biometric System-on-Card
Part 1: Core requirements
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46. Normative References
The minimum size of an area sensor shall be 169mm2
(13 × 13 mm or 0.512 × 0.512 in)
Allows fingerprint sensor, voice microphone, camera for facial image, signature input pad
In the case of a swipe sensor, the effective area of fingerprint
capture is bigger than the sensor size. A swipe sensor shall
have a minimum width of 13 mm (0.512 in)
ISO/IEC 17839-1:2014 - Biometric System-on-Card
Part 1: Core requirements
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47. Normative References
ISO/IEC 17839-3:2016 covers:
• Commands and data structures
• Internal Enrollment and External Enrollment
• Initiation of verification
• Status feedback
• Processing time management and extension
• Capability discovery mechanism
ISO/IEC 17839-3:2016 - Biometric System-on-Card --
Part 3: Logical information interchange mechanism
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48. Normative References
• ISO/IEC 24787:2010 - On-card biometric comparison
• ISO/IEC TR 30117:2014 - Guide to on-card biometric comparison
standards and applications
• ISO/IEC 18584:2015 - Conformance test requirements for on-
card biometric comparison applications
Other ISO TS / TR
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49. Normative References
Focus on EMV® 2nd Generation Specifications
• EMV 2nd Generation Specifications in progress:
• Biometric Terminal effective from Jan 1, 2018
• Biometric Card in progress, next meeting Paris Nov. 2017
• EMV 3-D Secure 2.0
• Specifies use of multiple options for step-up authentication, including biometrics
Visa, MasterCard and Amex have updated their Payment Applet specs
allowing Fingerprint Verification to be used as PIN alternative.
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50. Normative References
Payment service (PSD2) – Directive (EU) 2015/2366
• It provides the legal foundation for the further development of a better integrated internal
market for electronic payments within the EU.
• It puts in place comprehensive rules for payment services with the goal of making
international payments (within the EU) as easy, efficient and secure as payments within a
single country.
• Requires Strong Customer Authentication, using at least two independent elements
categorized as:
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Something I have
CARD
Something I know
PIN
Something I am
FINGERPRINT
INHERENCEPOSSESSIONKNOWLEDGE
51. Normative References
ICAO Doc Series 9303 – Machine Readable Travel Documents
• ICAO – Doc 9303 – Part 9: Deployment of Biometric Identification and Electronic Storage
of Data in eMRTDs
• ICAO – Doc 9303 – Part 10: Logical Data Structure (LDS) for Storage of Biometrics and
Other Data in the Contactless Integrated Circuit (IC)
Focus on storage / format / interoperability of biometric data into eMRTD.
No references to BSoC.
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52. Fingerprint Cards Acceptance Factors
CONSUMERS
Familiarity with smart cards
Familiarity with smartphones
Increasing acceptance for new technologies
Slow acceptance of mobile payments
LEGAL
Compliant with most regional / national legal
frameworks demanding biometric data not to be
stored in public DB.
EU GDPR effective May 2018 prohibits processing
of “Biometric Data”
INDUSTRY
Need to reduce Card-Present Fraud
Improve Identification / Verification processes
Growing focus on Safety/Security of physical assets
Minor or no infrastructure upgrade cost
Rising immigration control & MRTD verification
HEALTH
Risk of infection transmission with standalone
Fingerprint scanner (rotavirus, norovirus, hepatitis
A virus, respiratory syncytial virus, rhinovirus,
influenza virus, Shigella dysenteriae,
Enterohemorrhagic Escherichia coli, etc.)
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53. Fingerprint Cards Potential Blocking/Risk Factors
• Rejection due to personal reasons.
• Cultural incompatibility.
• Absence of respective biometric feature.
• Insufficient characteristics of the respective biometric feature.
• Abnormal characteristic of the respective biometric feature.
• Criminal organizations might develop techniques to fool fingerprint sensor (liveness
detector, blood detector, etc.).
• Because Biometric features are static, stolen (capture, cloned, forged) biometric cannot
be revoked or changed.
• Challenge to get product working in Hot / Dry / Dirty environment.
• Competing mobile payment technology may impact the smart card market.
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54. The Future
• Vein sensing capability
• Blood flow / heartbeat detection
• FPS IC + RF IC + MCU integrated into Smart Card Chip
• FPS under conductive plastic
• Contactless fingerprint
• Behavioural biometric
• DNA scanner
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57. References
- http://coe.int/en/web/conventions/full-list/-/conventions/treaty/005
- http://dhl.it/en/express/shipping/shipping_advice/lithium_batteries.html
- http://dmt.com.cn/product/aqkz/zwsb/529.html
- http://ec.europa.eu/justice/policies/privacy/docs/95-46-ce/dir1995-46_part1_en.pdf
- https://en.wikipedia.org/wiki/Biometrics
- https://en.wikipedia.org/wiki/Data_Protection_Directive
- https://en.wikipedia.org/wiki/General_Data_Protection_Regulation
- https://en.wikipedia.org/wiki/Fingerprint_recognition
- https://en.wikipedia.org/wiki/Personally_identifiable_information
- https://en.wikipedia.org/wiki/Supercapacitor
- http://fingertec.com/download/tips/whitepaper-01.pdf
- http://www.gemalto.com/govt/biometrics/biometric-data
- http://heritage.org/homeland-security/report/biometric-technologies-security-legal-and-policy-implications
- http://horizon2020projects.com/special-reports/technical-information-card-fraud-identity-theft-protection-
technologies/
- http://iata.org/whatwedo/cargo/dgr/Pages/lithium-batteries.aspx
- https://www.icao.int/Meetings/a38/Documents/WP/wp094_en.pdf
- https://www.icao.int/publications/pages/publication.aspx?docnum=9303
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58. References
- https://www.icao.int/publications/Documents/9303_p9_cons_en.pdf
- https://www.icao.int/publications/Documents/9303_p10_cons_en.pdf
- http://www.mainguet.org/
- https://ncbi.nlm.nih.gov/pubmed/19006507
- http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-122.pdf
- http://patentexaminer.org/wp-content/uploads/2011/09/SmartMetric.MC_.2.Complaint_small.pdf
- http://patentexaminer.org/wp-content/uploads/2011/09/SMME_v_MasterCard_1.pdf
- http://prba.org/wp-content/uploads/IATA-Lithium-Battery-Guidance-2017-1.pdf
- https://www.sec.gov/Archives/edgar/data/1301991/000114420415058083/v421136_ex10-8.htm
- https://scribd.com/doc/316791717/Special-Report-Biometrics-and-Border-Security
- https://uni-kassel.de/fb07/fileadmin/datas/fb07/5-Institute/IWR/Hornung/isse_2004_47-
57_biometric_identity_cards.pdf
- https://ups.com/media/news/en/ca/intl_lithium_battery_regulations.pdf
- https://www.youtube.com/watch?v=jw_4uaRNsBA&t=1228s
- https://www.youtube.com/watch?v=VESg5mPWk34&t=21s
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59. References
Struif, B.: Use of Biometrics for User Verification in Electronic Signature Smartcards. In: Attali, S., Jensen, T. (eds.) E-smart 2001. LNCS,
vol. 2140, pp. 220–227. Springer, Heidelberg (2001)
Murdoch, S.J., Drimer, S., Anderson, R., Bond, M.: Chip and PIN is broken. In: 2010 IEEE Symposium on Security and Privacy (2010)
Biometric Application Programming Interface (API) for Java Card, NIST/Biometric Consortium Biometric Interoperability, Assurance, and
Performance Working Group, version 1.1 (August 2002)
Henniger O., Nikolov D. (2013) Extending EMV Payment Smart Cards with Biometric On-Card Verification. In: Fischer-Hübner S., de Leeuw
E., Mitchell C. (eds) Policies and Research in Identity Management. IDMAN 2013. IFIP Advances in Information and Communication
Technology, vol 396. Springer, Berlin, Heidelberg
Privacy-preserving biometric authentication: challenges and directions, Elena Pagnin, Aikaterini Mitrokotsa Chalmers University of
Technology Gothenburg, Sweden (2017)
“EMV integrated circuit card specifications for payment systems – Book3: Application specification,” Version 4.2, June 2008.
Encyclopedia of Biometrics, Stan Z., Anil K. Jain, Springer Reference, ISBN: 978-3-642-27733-7
Anil K., Arun A. Ross – Introduction to Biometrics, Springer, ISBN: 978-0387773254
Nicholas M. Orlans John D. Woodward Jr., Peter T. Higgins – Biometrics, McGraw Hill Professional, ISBN: 978-0072222272
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60. Specs
ISO/IEC 7816-4:2013 Identification cards – Integrated circuit cards – Part 4: Organization, security and commands for
interchange
ISO/IEC 7816-11:2014 Identification cards – Integrated circuit cards – Personal verification through biometric methods
ISO/IEC 9797-2:2011 Information technology – Security techniques – Message Authentication Codes (MACs) – Part 2:
Mechanisms using a dedicated hash-function
ISO/IEC 17839-2:2015 Biometric System-on-Card: physical characteristics
ISO/IEC 18033-2:2006 Information technology – Security techniques – Encryption algorithms – Part 2: Asymmetric ciphers
ISO/IEC 18033-3:2010 Information technology – Security techniques – Encryption algorithms – Part 3: Block ciphers
ISO/IEC 19092:2008 -- Financial services -- Biometrics -- Security framework
ISO/IEC 19785-3 Information technology – Common Biometric Exchange Formats Framework – Patron format specifications
ISO/IEC 19794-1:2011 - Information technology -- Biometric data interchange formats -- Part 1: Framework
ISO/IEC 19794-2:2011 - Information technology –- Biometric data interchange formats –- Part 2: Finger minutiae data
ISO/IEC 19794-2:2011/Cor 1:2012 - XML encoding and clarification of defects
ISO/IEC 19794-2:2011/Amd 1:2013 - Conformance testing methodology and clarification of defects
ISO/IEC 19794-2:2011/Amd 2:2015 - Conformance testing methodology and clarification of defects
ISO/IEC 19794-3:2006 - Information technology -- Biometric data interchange formats -- Part 3: Finger pattern spectral data
ISO/IEC 19794-4:2011 - Information technology -- Biometric data interchange formats -- Part 4: Finger image data
ISO/IEC 19794-5:2011 - Information technology -- Biometric data interchange formats -- Part 5: Face image data
ISO/IEC 19794-6:2014 - Information technology -- Biometric data interchange formats -- Part 6: Iris image data
ISO/IEC 19794-7:2014 - Information technology -- Biometric data interchange formats -- Part 7: Signature/sign time series
data
ISO/IEC 19794-8:2011 -- Information technology -- Biometric data interchange formats -- Part 8: Finger pattern skeletal
data
ISO/IEC 19794-9:2011 -- Information technology -- Biometric data interchange formats -- Part 9: Vascular image data
ISO/IEC 19794-10:2007 -- Information technology -- Biometric data interchange formats -- Part 10: Hand geometry silhouette
data
EMV® Specification Bulletin No. 185 - First Edition March 2017 - Biometric Terminal Specification
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61. Patents (1/5)
CA 02581230 C Apparatus for fingerprint sensing and other measurements
CN 2010 20665177 非接触式包含指纹辨识的电子智能卡片 (Non-contact intelligent electronic card comprising
fingerprint identification )
CN 2010 20671893 非接触式电子式智慧卡片 (Non-contact electronic smart card )
CN 2015 20355127 智慧卡片指纹按压装置 (Wisdom card fingerprint press device)
CN 2016 02176914 U Fingerprint identification smart card
DE 2016 10101609 A1 Authentifikationsvorrichtung, Authentifikationsdokument und Verfahren zur
Authentifizierung einer Person
DE 2016 10201976 B3 Vorrichtung und Verfahren zur Bearbeitung eines Substrats
GB 2012 00019750 D0 Enrolling fingerprints by combining image strips to obtain sufficient width
GB 2013 00004639 D0 Fingerprint sensor
GB 2015 00022870 D0 Biometric smartcard with multiple modes of operation
GB 2015 00008294 D0 Biometric enrolment authorisation
GB 2015 00008296 D0 Contactless biometric identification device allowing multiple configurations
GB 2016 00000892 D0 Low thickness biometric card
GB 2016 00003099 D0 One-time password device
GB 2016 00004138 D0 Apparatus for testing a battery-powered device
GB 2016 00009572 D0 Smartcard and method for controlling a smartcard
GB 2016 00014998 D0 Biometric smartcard for providing feedback to a user
GB 2017 00004847 D0 Callibration method
GB 2017 00006460 D0 Bionetric smartcard
IT 2006 UD00028 A1 Carta Elettronica Universale Personale
IT 2006 UD00046 A1 Metodo Per Realizzare Una Scheda Personale Sicura E Suo Processo Di Funzionamento
IT 2006 UD00075 A1 A method for making a secure personal card and its working process
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62. Patents (2/5)
JP 2011 0105750 A ic card with fingerprint authentication
JP 2014 0529123 A Biometric authentication available smart card
JP 2016 0115098 A 指紋認証付スマートカードとこれを用いた決済方法
(Smart card with fingerprint authentication and settlement method using the same)
KR 2014 0164109 B1 RF Smart Combi-card Of Fingerprint Type And Dealing Method Thereof
KR 2015 0056437 RF Smart Card Of Fingerprint Type With Battery
KR 2015 0180446 B2 Smart Card Integrated With Thermoelement And Fingerprint Verification Chip As Laminated
Structure
KR 2016 0064133 A Smart Card Comprising Fingerprint Detecting Device And Method For Driving The Same
KR 2016 0090633 A Smart Card And A Fingerprint Recognition System Using Thereof
KR 2016 0125877 A RF Smart Card of Fingerprint Type with Battery
KR 2016 0129832 B1 Fingerprint Smart Card Having A Prevention Function Forged Fingerprint
TW 2010 0009718 A Method of manufacturing secure personal card and manufacturing processes thereof
TW 2016 0029842 A Fingerprint press device for smart card
TW 2016 0202811 U Switch structure of electronic module
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63. Patents (3/5)
US 1983 4621190 A Card with an IC module
US 1999 6325285 B1 Smart card with integrated fingerprint reader
US 2004 0129787 A1 Secure biometric verification of identity
US 2005 0178827 A1 Flexible fingerprint sensor arrays
US 2005 0212657 A1 Identity verification system with self-authenticating card
US 2006 0000892 A1 Method for biometric security using a smartcard
US 2006 0000899 A1 Method and system for dna recognition biometrics on a smartcard
US 2006 0113381 A1 Batteryless contact fingerprint-enabled smartcard that enables contactless capability
US 2007 0223179 A1 Fingerprint recognition smart card
US 2007 0228154 A1 System and method for sensing biometric and non-biometric smart card devices
US 2008 0164325 A1 Universal Smart Card
US 2008 0054875 A1 Biometric sensor and sensor panel
US 2008 0040615 A1 Biometric embedded devicer
US 2009 0084858 A1 Method For Making A Secure Personal Card And Its Working Process
US 2012 0241524 A1 Activation and indication of an RF field on a device including a chip
US 2013 0036463 A1 Biometric-enabled smart card
US 2013 0320464 A1 Integrally molded die and bezel structure for fingerprint sensors and the like
US 2015 0049925 A1 Method of manufacturing an electronic card
US 2015 0286855 A1 Fingerprint matching algorithm
US 2015 0371077 A1 Fingerprint recognition for low computing power applications
US 2015 0379250 A1 Secure biometric verification of identity
US 2017 0161528 A1 Smart card systems comprising a card and a carrier
US 2017 0213097 A1 Flexible Card with Fingerprint Sensor
US 2017 0213122 A1 Document with Sensor Means
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64. Patents (4/5)
US 2017 0228631 A1 Smartcard and method for controlling a smartcard
US 2017 0262692 A1 Capacitive Fingerprint sensing Device and Methon for Capturing a fingerprint using the
sensing device
USD 693874 S1 Electronic payment card with biometric verification means
USD 776664 S1 Smart card
USD 786355 S1 Smart card with a chip and finger-print sensor
USD 788847 S1 Smart card with a chip and finger-print sensor
USD 791772 S1 Smart card with a fingerprint sensor
WO 1998 052735 A1 Method of making smart cards
WO 2011 083241 A1 Multiple application chip card having biometric validation
WO 2006 080929 A1 Method of making a memory card by injection molding
WO 2013 155040 A1 Smart connect devices for the interconnectivity of data cards with computing devices to
enable the performance of various functions upon authentication by a user's fingerprint
and/or a user's photograph
WO 2013 160011 A1 Method of manufacturing an electronic card
WO 2016 016129 A1 Multi-layer composite for a security and/or value document and method for producing same
WO 2016 055660 A1 Contactless Biometric Identification Device Allowing Multiple Configurations
WO 2016 055661 A1 Biometric enrolment authorisation
WO 2016 055662 A1 Power load management
WO 2016 055663 A1 Power harvesting in a passive rfid device
WO 2016 055665 A1 Self-contained fingerprint identification device
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 64
65. Patents (5/5)
WO 2016 160816 A1 Smart data cards that enable the performance of various functions upon
activation/authentication by a user's fingerprint, oncard pin number entry, and/or by
facial recognition of the user, or by facial recognition of a user alone, including an
automated changing security number that is displayed on a screen on a card's surface
following an authenticated biometric match
WO 2016 190678 A1 Smart Card Including Fingerprint Detection Device And Driving Method Therefor
WO 2017 017085 A1 Smart card with main application and persistence application
WO 2017 025479 A1 Security protected passive rfid device
WO 2017 025481 A1 Power optimization
WO 2017 064097 A1 Multiple finger fingerprint authentication device
WO 2017 081268 A1 Value or security document made of a fiber composite material, and method for producing
the value or security document
WO 2017 089216 A1 Identification document and method for identifying an individual
WO 2017 093514 A1 Fingerprint card
WO 2017 093516 A1 Biometric card
WO 2017 102699 A1 Id token having a protected microcontroller
WO 2017 102984 A1 Device
WO 2017 109173 A1 Biometric device
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 65
66. Acronyms
2FA Two-Factors Authentication
3FA Three-Factors Authentication
ASIC Application-specific integrated circuit
AOC Authentication On Card
ASP Average Selling Price
BSC Biometric Smart Card
BSoC Biometric System on Cards
CBEFF Common Biometric Exchange File Format
COF Chip On Film
FPC Fingerprint Card
FPCB Flexible Printed Circuit Board
FPS Fingerprint Sensor
JV Joint Venture
MOC Match-on-Card
MCU Micro Controller Unit
NFC Near field communication
OTP One Time Password
PC Polycarbonate
PETG Polyethylene terephthalate
PII Personally Identifiable Information
PSD2 Payment Service Directive
PVC Polyvinyl chloride
RFID Radio-frequency identification
SC Supercapacitor
SE Secure Element
SW Software
TOC Template-On-Card
5-6 October 2017
Milan - Italy
The Rise of Biometric Cards | state of the art and future challenges for card manufacturers | by Antonio D’Albore | Embedded Security News 66