NIST facial recognition evaluations showcase accuracy gains, new developers #biometrics #facialrecognition #NIST National Institute of Standards and Technology (NIST) SANSAP TECHNOLOGY PRIVATE LIMITED
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Thanks to Biometric Update for this explanation of National Institute of Standards and Technology (NIST) #FRTE and #FATE. Paravision has also produced this very handy overview of how to read the #NIST results, including a video tutorial... https://lnkd.in/ep6Zhmuy
What are NIST evaluation tests for facial recognition algorithms? #biometrics #facialrecognition #nist
What are NIST evaluation tests for facial recognition algorithms?
https://www.biometricupdate.com
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Micah Willbrand thanks so much for sharing this and highlighting one our greatest strengths. 🎉 🎊 🙌 However, working for NEC, I'm not a bit surprised! NEC has been around for over 120 years, and still provides some of the most advanced technology in our modern-day era. It invests billions each year into R&D to ensure that we are number 1 in what we do. Our most important attribute for our facial biometrics is our accuracy, whether its identifying twins, person with facial coverings, or even someone that enrolled their face years ago that can still be identified. 👶 🧓 The security of PII, Personal Identifiable Information, is at the core of what we do. We convert your photo into a template that NEC and only NEC can decipher. An individual's photo is never stored, even at the enrollment process. 👨💻 💪 We are trust by US CBP, Universal, Delta, and many others, and Homeland Security has given as a 100% accuracy. 👮♀ It is a truly is a state-of-the-art solution for facial biometrics, in both digital and physical enrollment, verification, and access. The path to digitization is ever evolving, but if you haven't started looking into facial biometrics, and digital ID, perhaps now is the time to reach out for a discussion as to whether this solution is the next step for you. #NECAM #NEC #facialrecognition #biometrics #biometricsecurity #digitalidentity #nist #1
CEO | GM | MD | CPO Biometric and Identity executive revolutionizing a consumer experience to make the world a better place
BOOM 🚀 National Institute of Standards and Technology (NIST) Released its most recent evaluation on #FRTE (Face Recognition Technology Evaluation). NEC Corporation of America was top-3 in all evaluations but what makes me most excited? No, not being #1 in databases of more than 12 million records (which has been pretty standard for more than a decade). Its this: #1 in Facial Recognition matching on photos greater than 12 years apart. And it wasn't even close - beating the closest competitor by over 50% and number 5 by 210%. Why does that matter? You know that annoying thing most vendors ask customers to re-enroll their photos constantly? NEC doesn't - one and done because our system is built to handle aging and physical changes over time. Worried about account take over fraud? Just compare it against the template you have from 2013. It'll still match. Now you know why one of the worlds largest social networks uses NEC technology for securing their accounts. NEC has the most diverse, robust and tested biometrics on the planet. Come chat with myself, Mike Salazar, Anne-Marie Soto, Ph.D., Ralph Awika, Greg Keegstra, Kevin Ells or any other NEC team member and be part of the revolution! #facialrecognition #facialbiometrics https://lnkd.in/eCRHppxB
Face Recognition Technology Evaluation (FRTE) 1:N Identification
pages.nist.gov
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What is Facial Recognition and How it Works?
What is Facial Recognition and How it Works?
https://www.supperbtech.com
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At Aware, we strive to improve performance and adapt to emerging threats without compromising customer experience or fairness. And we're proud to share that our recent top performer rank in the National Institute of Standards and Technology (NIST) FATE Benchmarking Test demonstrates that commitment to delivering the highest level of technology with accurate, non-biased, and fast algorithms packaged in full systems that are easily configurable to balance between low friction and high security. In case you missed the recent news: Aware’s facial presentation attack detection (PAD) algorithms achieved top ranking among 82 tested systems in the newly introduced NIST Face Analysis Technology Evaluation (FATE) test - NIST IR 8491. The independent NIST results show Aware as a leader in presentation attack detection liveness technology while affirming Aware’s leadership position in optimizing demographic parity. See more about the NIST FATE results in the official release from Aware: https://lnkd.in/e27YD6kd #Biometrics #Bias #Tech #AI
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How the identity document verification industry is navigating the challenge of document spoofing which has intensified after generative AI. Remote digital onboarding is a norm in a rapidly developing digital world. Businesses required to verify the identity of clients and partners are using advanced software solutions to verify identity documents. As technologies to verify documents have advanced, tools to forge documents with sophistication, have also advanced accordingly. Generative AI is proving to be the perfect partner in crime for those already churning out fake documents with greater accuracy. Accurate document verification is needed more than ever in the era of digital identities that require the robust authentication of government-issued identities. State institutes, regulatory bodies, and standard-setting institutions also need to devise guidelines on methods to accurately verify identities while thwarting dangers of deepfakes. To talk about how the document verification industry is navigating the challenge Wil Janssen, Co-founder, and Chief Revenue Officer at ReadID by Inverid will speak on our webinar, Countering Document Spoofing: Innovations in Detecting Synthetic IDs. With a PhD from the University of Twente and a background in Computer Science from the University of Eindhoven, Wil Janssen brings extensive expertise as an advisor, inspirator, and researcher in the realms of innovation and IT. Serving as the Chief Revenue Officer (CRO) at Inverid, he spearheads marketing and sales strategies. His prolific career is underscored by over 70 publications in various esteemed magazines and newspapers. Moreover, he co-founded Inverid in 2013, showcasing his entrepreneurial acumen and commitment to advancing technological solutions. To hear from Will Janssen and know about Inverid's approach of ensuring accurate document verification, Register here for free: 👉 https://lnkd.in/dvMQxfhn #IDVerification #Documentverification
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Senior Special Advisor & Programs Delivery | Business Advisory & Tech Innovation | AI, Biometrics, Identity, Cyber, Data | GAICD
Facial Liveness test performance can be quite discriminatory for certain skin colours, but also sometimes genders and in some cases even ethnicities (I remember the odd case of Sikh turbans being unduly considered as potential presentation attacks). There’s been a lot of progress on the fairness of facial matching algorithms but the performance of liveness tests across demographics is still lagging, and it has discriminatory implications. Here’s a research paper advancing the topic - because to improve it we need to be able to measure it simply. Hopefully the US NIST will include something similar in its benchmarks. #livenessDetection #PresentationAttackDetection #facialrecognition #fairness
Have you thought if face presentation attack detection (PAD, anti-spoofing) protects different genders to the same degree? What about other facial attributes? What causes this performance inequality? Is it the imbalanced training data or might it be also the assignment of the decision threshold itself? How can we reduce the difference in the performance between different demographic or non-demographic groups while maintaining or even enhancing high accuracy? Knowing that some solutions can be extremely accurate but unfair, while others can be fair but produce very faulty decision, how can we represent both the performance AND its equality in one metric? In our recently published work at Pattern Recognition (Elsevier) we address all these questions and a bit more : ) - We present the Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels - We comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions - We propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance - To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues All that and more is described in details in the paper: https://lnkd.in/eyJ8zgiR The data and the implementations are available for research purposes under: https://lnkd.in/emtP5w9j Great job Meiling Fang! Great collaboration with Vitomir Štruc! And of course, big thanks to the whole biometric team at Fraunhofer IGD Fadi Boutros, Biying Fu, Marco Huber, and Jan Niklas Kolf! #research #innovation #biometrics #security #computervision #syntheticdata #artificialintelligence #machinelearning #responsibleai #facerecognition #antispoofing #patternrecognition Fraunhofer IGD Technische Universität Darmstadt ATHENE-Center
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Have you thought if face presentation attack detection (PAD, anti-spoofing) protects different genders to the same degree? What about other facial attributes? What causes this performance inequality? Is it the imbalanced training data or might it be also the assignment of the decision threshold itself? How can we reduce the difference in the performance between different demographic or non-demographic groups while maintaining or even enhancing high accuracy? Knowing that some solutions can be extremely accurate but unfair, while others can be fair but produce very faulty decision, how can we represent both the performance AND its equality in one metric? In our recently published work at Pattern Recognition (Elsevier) we address all these questions and a bit more : ) - We present the Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels - We comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions - We propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance - To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues All that and more is described in details in the paper: https://lnkd.in/eyJ8zgiR The data and the implementations are available for research purposes under: https://lnkd.in/emtP5w9j Great job Meiling Fang! Great collaboration with Vitomir Štruc! And of course, big thanks to the whole biometric team at Fraunhofer IGD Fadi Boutros, Biying Fu, Marco Huber, and Jan Niklas Kolf! #research #innovation #biometrics #security #computervision #syntheticdata #artificialintelligence #machinelearning #responsibleai #facerecognition #antispoofing #patternrecognition Fraunhofer IGD Technische Universität Darmstadt ATHENE-Center
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The latest research on the "Law Enforcement Biometrics Market" reveals promising growth and development strategies for the period 2023-2030. Biometrics technology, which analyzes physiological or behavioral patterns for authentication or identification, is increasingly used in law enforcement, banking, and physical access. This technology, which includes fingerprints, irises, voice patterns, and facial geometry, provides an extra layer of security for data access and individual identification. At Acquire.AI, we understand the importance of such advanced technologies. Our voice biometrics and speech analytics solutions are designed to enhance security and efficiency, reflecting our commitment to quality, innovation, and reliability. As the market evolves, we continue to provide AI solutions that meet the changing needs of businesses. #ArtificialIntelligence #Biometrics #AcquireAI
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Title Advancements in Face Recognition Technology: A Comprehensive Overview Introduction: Face recognition technology has rapidly evolved in recent years, transforming the landscape of security, authentication, and user experience. This article explores the latest advancements in face recognition technology, shedding light on its applications, challenges, and ethical considerations. Facial Recognition Basics: Define the fundamental principles of facial recognition technology Explore the key components, including facial feature extraction and matching algorithms Applications Across Industries Discuss how face recognition is being employed in various sectors, such as security, finance, healthcare, and retail Highlight specific use cases and success stories in each industry Security and Authentication Examine the role of face recognition in enhancing security measures Discuss its integration in access control systems, surveillance, and biometric authentication Challenges and Limitations Address the challenges associated with face recognition, including accuracy, bias, and privacy concerns Explore ongoing research and initiatives aimed at mitigating these challenges. Ethical Considerations Delve into the ethical implications of widespread face recognition usage Discuss concerns related to surveillance, consent, and potential misuse of the technology Emerging TrendsHighlight the latest trends shaping the future of face recognition technology Discuss innovations, such as 3D facial recognition and emotion detection Regulatory Landscape Provide an overview of the regulatory framework surrounding face recognition globally Discuss how governments and organizations are addressing privacy and security concerns through legislation Future Prospects Speculate on the future developments and applications of face recognition Consider potential breakthroughs and their impact on society Case Studies Present notable case studies demonstrating the practical implementation and benefits of face recognition technology Conclusion Summarize key takeaways from the article. Emphasize the need for a balanced approach that considers technological advancements alongside ethical and privacy considerations This comprehensive overview aims to provide readers with a nuanced understanding of face recognition technology, from its foundational principles to its real-world applications and the ethical considerations that come with its widespread adoption #snsinstitutions #snsinstitutions #facerecognition #snsdesignthinkers #designthinking
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In today’s digital landscape, robust identity proofing and verification is paramount. To address the rising security challenges, the adoption of AI technology has ushered in a new era of identity verification trends. Some of the fastest risers include: ☝️Facial recognition: Facial recognition technology has advanced significantly in recent years, allowing for more accurate and reliable identification. This technology uses algorithms to analyze unique features of a person’s face, such as the distance between the eyes or the shape of the jawline, to create a digital template that can be compared against a database of known faces. Facial recognition is becoming increasingly common for security and authentication purposes, such as unlocking devices or verifying identities for financial transactions.
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