slug: abistemplate

definition: System for large-scale biometric matching and identification using algorithms and databases.

question: What is an Automated Biometric Identification System (ABIS)?

answer: An ABIS is a system that automates large-scale biometric matching and identification using algorithms and centralized databases.

question: How does ABIS improve biometric matching?

answer: ABIS leverages advanced algorithms to process and compare biometric data at scale, reducing manual verification and improving throughput.

question: What are common applications of ABIS?

answer: Typical use cases include criminal investigations, border security, and large-scale identity verification in government programs.

dataset: glossary

sourceType: markdown


slug: ai-agent-identity

title: AI Agent Identity

definition: Identity, authentication, and authorization controls used to govern AI agents or AI-enabled accounts that can act on behalf of people, organizations, or systems.

question: Why do AI agents need identity controls?

answer: AI agents may access data, execute tasks, or make decisions, so systems need to know which user or organization authorized the agent and what it is allowed to do.

question: How does biometric access relate to AI agents?

answer: Biometric or strong local authentication can be used to authorize sensitive agent actions, especially when the agent is operating on a user's device or enterprise endpoint.

question: Is AI agent identity the same as human identity proofing?

answer: No. Human identity proofing establishes who the person is; AI agent identity controls establish the agent, its authority, its session, and its allowed actions.

label: ID Tech: Microsoft Project Solara biometric access for AI agents


slug: consent

title: Consent

definition: Voluntary agreement by a person for their personal data to be used for specific purposes.

question: What does consent mean in data privacy?

answer: Consent is a person’s voluntary and informed agreement for their personal data to be collected, processed, and used for specific purposes.

question: Why is consent important for biometric data?

answer: Biometric data is sensitive; obtaining consent ensures compliance with privacy laws and respect for individual autonomy.

question: How can organizations obtain valid consent?

answer: Valid consent requires clear information, unambiguous opt-in mechanisms, and allows individuals to withdraw consent at any time.


slug: deep-learning

title: Deep Learning

definition: Subset of machine learning using neural networks with multiple layers to model complex data representations.

question: What is deep learning?

answer: Deep learning is a subset of machine learning that uses neural networks with multiple layers to model complex patterns and representations.

question: How is deep learning used in biometric systems?

answer: Techniques like convolutional neural networks (CNNs) extract high-dimensional features from biometric data (e.g., face, fingerprint) to improve recognition accuracy.

question: Why is deep learning important for biometric matching?

answer: Deep learning models automatically learn discriminative features, reducing manual feature engineering and enhancing performance across varied conditions.


title: Equal Error Rate (EER)

slug: eer

definition: A single operating point where the false match rate equals the false non‑match rate. Lower EER generally indicates better discriminative performance for a matcher on a given dataset.

related: facial-recognition fingerprint-recognition iris-recognition multimodal-biometrics


slug: eidas

title: eIDAS

definition: EU regulation on electronic identification and trust services for secure electronic transactions.

question: What is eIDAS regulation?

answer: The eIDAS Regulation is an EU framework for electronic identification and trust services, setting standards for secure online transactions and legally recognized electronic signatures.

question: How does eIDAS impact digital identity services?

answer: eIDAS ensures cross-border interoperability of digital identity schemes, enabling secure authentication and electronic signatures across EU member states.

question: What are the assurance levels defined by eIDAS?

answer: eIDAS defines assurance levels (low, substantial, high) based on the rigor of identity proofing and authentication methods required for different security needs.


title: Failure to Acquire (FTA)

slug: failure-to-acquire

definition: The proportion of attempts where a usable biometric sample cannot be captured (e.g., sensor or user issues). FTA reduces overall system throughput and may bias evaluations if not accounted for.

related: fingerprint-recognition facial-recognition iris-recognition behavioral-biometrics


title: Failure to Enroll (FTE)

slug: failure-to-enroll

definition: The proportion of subjects who cannot be enrolled due to poor quality or insufficient data. High FTE indicates capture or process issues that limit coverage of the intended population.

related: fingerprint-recognition facial-recognition iris-recognition abis-dedup


slug: fido

title: FIDO Alliance

definition: Industry consortium promoting standards for strong authentication like U2F and FIDO2.

question: What is the FIDO Alliance?

answer: The FIDO Alliance is an industry consortium that develops open authentication standards to reduce reliance on passwords and enhance security with strong, phishing-resistant methods.

question: What authentication protocols does FIDO support?

answer: FIDO supports protocols like U2F (Universal 2nd Factor) and FIDO2, which use public key cryptography and hardware or platform authenticators.

question: How does FIDO improve online authentication security?

answer: By replacing shared secrets with unique cryptographic keys tied to a user’s device and requiring user presence, FIDO mitigates phishing and credential theft.


title: FMR and FNMR

slug: fmr-fnmr

definition: FMR (False Match Rate) is the proportion of impostor comparisons incorrectly accepted; FNMR (False Non‑Match Rate) is the proportion of genuine comparisons incorrectly rejected. They trade off with the decision threshold.


slug: gdpr

title: General Data Protection Regulation (GDPR)

definition: EU regulation enforcing data protection and privacy for individuals within the European Union.

question: What is the GDPR?

answer: The General Data Protection Regulation (GDPR) is an EU regulation that governs the collection, processing, and protection of personal data for individuals within the European Union.

question: How does GDPR treat biometric data?

answer: Under GDPR, biometric data is classified as sensitive personal data, requiring explicit consent, strict security measures, and legal justification for processing.

question: What rights do individuals have under GDPR?

answer: GDPR grants rights such as access, rectification, erasure, data portability, and the right to object to processing of personal data.


title: Identification vs Verification

slug: identification-vs-verification

definition: Identification answers ‘who is this?’ via 1:N search against a gallery. Verification answers ‘is this person who they claim?’ via 1:1 comparison between a probe and a claimed identity’s reference.

related: abis-dedup facial-recognition fingerprint-recognition passkeys-webauthn


slug: identity-proofing

title: Identity Proofing

definition: Process of verifying that a person is who they claim to be, often using ID documents and biometric checks.

question: What is identity proofing?

answer: Identity proofing is the process of verifying that a person is who they claim to be, often using documents, biometric checks, and trusted data sources.

question: Why is identity proofing essential for digital services?

answer: It prevents identity fraud by ensuring only legitimate users can enroll and access services, supporting security and regulatory compliance.

question: Which methods are used for identity proofing?

answer: Common methods include document verification, facial recognition with liveness detection, and database checks against authoritative records.


slug: kyc-aml

title: KYC / AML

definition: Know Your Customer and Anti-Money Laundering controls used by regulated organizations to verify customers, monitor risk, and prevent financial crime.

question: What does KYC verify?

answer: KYC checks a customer's identity and risk attributes, often through document verification, biometric selfie checks, sanctions screening, and database checks.

question: How does AML relate to KYC?

answer: AML programs use KYC data plus transaction monitoring and screening to detect and prevent money laundering and related financial crime.

question: Why is KYC important for stablecoin and payment platforms?

answer: Payment and digital-asset platforms often need identity verification, sanctions controls, and ongoing monitoring to satisfy regulatory and banking requirements.

label: ID Tech: Sumsub and Reap stablecoin payment compliance

label: ID Tech: Sumsub and iMind South Korea payment compliance


slug: live-facial-recognition

title: Live Facial Recognition

definition: Real-time facial recognition that compares faces captured from live camera feeds against a watchlist or enrolled gallery.

question: How is live facial recognition different from retrospective face search?

answer: Live facial recognition runs against real-time camera feeds, while retrospective search analyzes previously captured images or video.

question: Why is live facial recognition controversial?

answer: It can identify people in public spaces, so governance concerns include legal authority, proportionality, watchlist accuracy, retention, oversight, and redress.

question: Is live facial recognition always identification?

answer: Most public-space live deployments are 1:N identification because they compare unknown faces against a gallery or watchlist.

label: ID Tech: Met Police static live facial recognition

label: ID Tech: Kenya national-ID-linked CCTV proposal


slug: matching

title: Biometric Matching

definition: Comparing a biometric sample against stored templates to verify or identify an individual.

related: abistemplate template

question: What is biometric matching?

answer: Biometric matching compares a captured biometric sample against stored templates to verify or identify an individual based on similarity scores.

question: What are verification and identification in biometric matching?

answer: Verification (1:1) confirms a claimed identity, while identification (1:N) searches a database to find a matching identity without a prior claim.

question: What factors influence matching accuracy?

answer: Accuracy depends on sample quality, sensor reliability, algorithm robustness, environmental conditions, and threshold settings.


slug: mobile-drivers-license

title: Mobile Driver's License (mDL)

definition: A digital driver's license stored in a mobile wallet or app, commonly implemented using ISO/IEC 18013-5 for credential presentation and verification.

question: How does an mDL differ from a scanned driver's license?

answer: An mDL is a cryptographically verifiable credential, while a scan or photo is only an image of a document and requires separate authenticity checks.

question: What standard is commonly associated with mDLs?

answer: ISO/IEC 18013-5 defines mobile driver's license presentation and verification methods.

question: Why do platforms use mDLs?

answer: Platforms can request specific attributes, such as age or license status, without necessarily collecting the full document image.

label: ID Tech: Uber scales Apple's Verify with Wallet


slug: multi-factor-authentication

title: Multi-Factor Authentication (MFA)

definition: Authentication method requiring two or more verification factors, such as something you know and something you are.

question: What is multi-factor authentication (MFA)?

answer: MFA is an authentication method requiring two or more verification factors, such as something you know (password), something you have (token), and something you are (biometric).

question: How does MFA improve security?

answer: By combining independent factors, MFA reduces the risk of unauthorized access if one factor is compromised, strengthening overall protection.

question: What are common MFA examples?

answer: Examples include password plus SMS OTP, hardware tokens with PIN, smartphone push notifications, and biometric factors like fingerprint or face recognition.


slug: nist-frvt

title: NIST Face Recognition Vendor Test (FRVT)

definition: A series of evaluations by NIST to benchmark face recognition algorithms under controlled and uncontrolled conditions.

question: What is NIST FRVT?

answer: The NIST Face Recognition Vendor Test (FRVT) is a benchmarking program evaluating face recognition algorithm performance under various conditions.

question: How does NIST FRVT assess face recognition?

answer: FRVT measures accuracy, speed, and interoperability by testing algorithms on standard datasets and reporting metrics like false match and non-match rates.

question: Why are NIST FRVT results important?

answer: Unbiased FRVT results guide organizations in selecting reliable face recognition solutions by providing standardized performance benchmarks.


title: On‑Device vs Server‑Side Matching

slug: on-device-vs-server-matching

definition: Design choice where biometric matching happens locally on a user’s device (with templates kept device‑bound) versus centrally on a server. On‑device improves privacy and reduces breach scope; server‑side can enable centralized management and large‑scale search (1:N).


slug: pad

title: Presentation Attack Detection (PAD)

definition: PAD refers to techniques used to detect artefacts (masks, photos, deepfakes) intended to spoof biometric systems.

related: facial-recognition fingerprint-recognition voice-recognition iris-recognition

question: What is Presentation Attack Detection (PAD)?

answer: PAD refers to techniques used to detect and prevent spoofing attacks by verifying that a biometric sample originates from a live human rather than artificial artefacts.

question: Why is PAD critical for biometric systems?

answer: PAD ensures security by preventing attackers from using masks, photos, or deepfake videos to spoof biometric authentication.

question: What methods are used for PAD?

answer: Common methods include texture analysis, motion/liveness detection, infrared imaging for vein patterns, and challenge-response protocols.


slug: privacy

title: Privacy

definition: The right of individuals to control the collection, use, and disclosure of their personal data.

question: What does privacy mean in biometrics?

answer: Privacy in biometrics refers to individuals’ rights to control how their biometric data is collected, processed, stored, and shared.

question: How do privacy regulations protect biometric data?

answer: Regulations like GDPR and CCPA enforce data minimization, consent requirements, and security controls for handling biometric information.

question: What are best practices for ensuring biometric privacy?

answer: Best practices include encryption of data at rest and in transit, anonymization, strict access controls, transparent policies, and user consent.


slug: proof-of-life

title: Proof of Life

definition: A verification process used to confirm that a person is alive and present, often for pensions, benefits, account recovery, or high-risk authentication.

question: Why do pension programs use proof-of-life checks?

answer: They help prevent payments to deceased or ineligible recipients while reducing the need for in-person visits.

question: How are biometric proof-of-life checks performed?

answer: A user may submit a selfie or short video, which is checked for face match and liveness before the program records the verification.

question: What are the main risks?

answer: Risks include false rejection, poor accessibility, weak liveness detection, fraud by proxy, and exclusion of users without suitable devices.

label: ID Tech: Pakistan facial recognition app for pensioners


slug: reusable-digital-identity

title: Reusable Digital Identity

definition: An identity model in which a verified user, credential, or attribute can be reused across multiple services instead of repeating a full identity check each time.

question: How is reusable digital identity different from one-time verification?

answer: One-time verification proves identity for a single service event; reusable identity lets the user present a previously verified identity or attribute to other services.

question: What technologies support reusable identity?

answer: Mobile wallets, verifiable credentials, reusable KYC platforms, identity provider networks, and trust frameworks can all support reusable identity.

question: What are the benefits and risks?

answer: Benefits include less friction and fewer repeated document checks. Risks include over-collection, correlation across services, governance failures, and weak revocation.

label: ID Tech: Bhutan NDI-based digital consent


title: ROC and DET Curves

slug: roc-det

definition: Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) curves visualize biometric performance across thresholds. ROC plots true accept vs false accept; DET plots false reject vs false accept on normal deviate scales for readability.

related: fmr-fnmr facial-recognition fingerprint-recognition iris-recognition


slug: spoofing

title: Biometric Spoofing

definition: Attempt to deceive a biometric system by presenting artificial traits like masks or fake fingerprints.

question: What is biometric spoofing?

answer: Biometric spoofing is the act of deceiving a biometric system by presenting fake traits like silicone fingerprints, printed face images, or masks.

question: How do systems detect spoofing attacks?

answer: Detection uses PAD techniques, liveness detection algorithms, multi-modal biometrics, and machine learning classifiers to identify artefacts.

question: What are the security risks of biometric spoofing?

answer: Successful spoofing can grant unauthorized access, compromise security, and undermine trust in biometric authentication solutions.


title: Template Inversion

slug: template-inversion

definition: An attack that attempts to reconstruct a biometric sample (e.g., a face image or fingerprint) from its stored template or embedding. Strong template protection schemes aim to make inversion computationally infeasible or yield unusable reconstructions.


slug: template

title: Biometric Template

definition: Digital representation of biometric features extracted from a sample for matching and storage.

question: What is a biometric template?

answer: A biometric template is a digital representation of extracted features (e.g., fingerprint minutiae or facial embeddings) used for matching and storage.

question: How are biometric templates protected?

answer: Templates are secured through encryption, hashing, cancelable biometric schemes, and secure hardware to prevent theft and misuse.

question: Why use templates instead of raw biometric data?

answer: Templates abstract away raw images, reducing storage requirements, protecting privacy, and enabling fast and secure comparisons.


title: Universal 2nd Factor (U2F)

definition: Open authentication standard that adds a second factor using hardware tokens for stronger security.

question: What is Universal 2nd Factor (U2F)?

answer: U2F is an open authentication standard by the FIDO Alliance that uses hardware tokens (USB/NFC) to provide phishing-resistant two-factor authentication.

question: How does U2F prevent phishing attacks?

answer: U2F uses origin-bound cryptographic challenge-response, ensuring tokens only authenticate legitimate websites and thwarting phishing attempts.

question: Can one U2F device be used with multiple services?

answer: Yes, U2F tokens generate unique key pairs per service, allowing secure, cross-platform authentication with a single physical device.