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AI facial recognition systems for KYC
Introduction: why Face-KYC and where its boundaries are
Identity verification is a basic requirement for financial and iGaming services. Face-KYC (face recognition in conjunction with documents) speeds up onboarding, reduces fraud and makes checks reproducible. But this is personal biometric data, so the architecture should be "privacy-first": minimization, explicit consent, encryption, retention and transparent explanations of decisions. The technical goal is to provably establish that the camera is a living person, not a mask/video, and that it coincides with the photo in the document.
1) Data and collection: what you really need
Selfie video frames (short clip or series of frames) for livnes and face embedding.
Photo/scans of the document (passport/ID/water. credential) + MRZ/QR/chip zones.
Metadata: device type, illumination, focus, exposure, face geometry.
Consent logs: explicit consent to biometrics, retention/deletion policy, processing goals.
Principles: PII minimization, encryption "on the wire" and "on the disk," separation of keys and data, TTL/retention, access by the least rights (RBAC/ABAC).
2) Livnes detection (PAD): How to tell a living face from a fake
The goal of PAD (presentation attack detection) is to prove that there is a living subject in front of the camera, and not a photo, video on the screen, mask, 3D layout or deepfake.
Methods:- Passive (silent): micromovement analysis, parallax, glare/reflexes, texture/moire, depth hints from one camera, photometric anomalies.
- Active (prompted): follow the point with a glance, blink/smile, turn your head, count out loud (if possible - without audio biometry in "hard" jurisdictions).
- Multi-sensor (optional): TrueDepth/IR/ToF, "structured light," stereo.
- Anti-reentrance: protection against scrolling prerecorded reactions (instruction/timing randomization).
Attack signals: paper photo, smartphone/tablet screen (moire, glare), masks (albedo/edge artifacts), deepfake traces (inconsistency in eyes/teeth/borders).
Exit: Speed of livness + cause (XAI flags), thresholds are adjusted by jurisdiction and risk.
3) Selfie ↔ document matching: leak-free accuracy
1. OCR/MRZ/chip: extract photo and document fields; validate checksums, date/country/type.
2. Face detection & alignment: find a face on a selfie and in a document, normalize posture/illumination.
3. Face embeddings: convolutional/transformational embeddings with training on large datasets, but with fine-tune on domain frames (mobile, bad light).
4. Comparison: cosine proximity/Euclidean + adaptive thresholds (taking into account frame quality, posture, age shift).
5. Docking: document integrity validation (holograms/GPU patterns/microprinting for high-risk streams), search for signs of forgery.
Result: probabilistic match-score with a confidence interval and explainable quality features.
4) Orchestrator of solutions: "zel ./Yellow ./Red."
Green: high rainfall and match, the document is valid → auto-app, accounting/raising limits.
Yellow: moderate risk (low light, partially hidden face, controversial match) → soft pre-verification: repetition with prompts, replacement of device/lighting, request for a second document.
Red: explicit PAD/forged document/mismatch of → feet, manual check (HITL), recording of incident.
All solutions are written in audit trail with model versions, thresholds and XAI explanations.
5) Quality metrics: what to measure and show
Liveness: APCER/BPCER (attack acceptance/rejection errors), ACER, EER; separately - for different types of attacks (print/replay/mask/deepfake).
Face match: FAR/FRR, ROC/DET curves, TPR@FAR=10⁻⁴... 10⁻⁶ for high-risk threads.
Frame quality: proportion of resamples, distribution of postures/lightening/occlusions.
Fairness: breakdown of errors by gender/age/skin types/devices and lighting (balanced error rates).
Operating: average onboarding time, auto-app share, HITL share, retries, NPS/KYC-CSAT.
6) Fairness and accessibility: not just accuracy
Bias audits: regular reports on segments and shooting scenarios; mixing in underrepresented groups during training/validation.
A11y-UX: large prompts, gestures, subtitles, voice instructions, quiet mode, support for weak devices and low light.
Edge-friendly: on-device preprocessing (frame gluing, quality detection) with loading only the necessary fragments.
7) Privacy by Design and Compliance
Minimization and purpose limitation: use biometrics only for KYC and only as much as necessary; separate storage of biometrics and personal data.
Shelf life: short TTL selfie/video; long-term - only hash embeddings/decision log, if allowed.
Rights of the data subject: access/deletion/challenge of the decision; understandable request channels.
Model/version tracking: full lineage, test script reproducibility.
Jurisdictions: processing boundaries (local regions), feature flags for different regulatory regimes.
8) Anti-fraud integration: where Face-KYC has the greatest effect
Multiaccounting: graph of connections by devices/payments + Face-dedup on embeddings (with strict limits and legal basis).
Account Takeover: Quick Face-re-verify again when changing device/geo/payment method.
Chargeback/bonus abuse: linking KYC levels to limits and auto payments; "green" - instant cashout.
9) Attacks and defense: what threatens and how to defend
Replay and print attacks: detection of moiré/speculators/flatness; active prompts.
Masks/3D layouts: albedo/edge/speculator analysis; depth/IR, if any.
Deepfakes: detection of incosystems (blink/gaze/teeth/skin), generation artifacts, audio-lip-blue (if sound is used).
Injection attacks in the video pipeline: trusted SDKs, environment attestation, packet signing, device binding protection.
Attacks on the model: drift monitoring, adversarial-robustness checks, canary samples.
10) MLOps/QA: production discipline
Versioning of dates/features/models/thresholds; clear data schemas.
Continuous calibration for devices/lighting/regions, shadow rolling, rollback.
Client reliability: offline buffer, retrays with a weak network, detection of "stuck" frames.
Chaos-engineering of video/light/frame misses: the system should degrade gently, not "fall."
Sandboxes for audit: replay verifications with XAI logs, stands for the regulator.
11) UX "pain-free": How to reduce failures
Interactive "traffic-light" quality (light/distance/face frame).
Tips before shooting and super short active check (≤5 -7 seconds).
Transparent statuses: "instantly/need a second attempt/manual check" + reason in understandable language.
Respectful tone: no threats and "wait 72 hours" - always with ETA.
12) Implementation Roadmap (8-12 weeks → MVP; 4-6 months → maturity)
Weeks 1-2: requirements/jurisdictions, Privacy by Design, SDK/sensor selection, UX layouts, baseline metrics.
Weeks 3-4: storm v1 (passive), face-match v1, OCR/MRZ, safe storage, version logging.
Weeks 5-6: active clues, XAI explanations, anti-fraud/limits integration, A/B UX.
Weeks 7-8: fairness audit, drift monitoring, auditor sandbox, HITL playbooks.
Months 3-6: multisensor/IR (where acceptable), deepfake detection, edge optimization, federated learning, local storage regions.
13) Frequent mistakes and how to avoid them
Rely only on active challenges. Combine passive signals and quality gates.
Ignore lights/devices. Test on cheap cameras and low light; give clues.
There are no fairness controls. Segment errors undermine legal stability and trust.
Store "raw materials" for too long. Shorten TTL, use embeddings/hashes.
Without XAI. Unexplained refusals → complaints/fines.
Monolith without rollback. Any update without A/B/shadows is a risk of massive KYC files.
AI-Face-KYC works when it is a system, not a "recognition library": showers + fair match of faces, transparent decisions, strict privacy and MLOps discipline. Such a circuit simultaneously speeds up the onboarding of honest users, reduces fraud and retains the trust of the regulator and customers. The key principles are minimization of data, explainability, fairness and safe exploitation throughout the entire life cycle.