Examination Entry · Identity · Security
ExamGuard connects identity verification, hall-ticket validation, seating, evidence collection, attendance and security into one auditable examination entry workflow.
ExamGuard Verification
AI Perception
Face + Liveness
Human Review
Always Available
Audit Trail
Every Decision
Provider Agnostic
Swappable AI
7-Stage
Verification
Verification Pipeline
Every examination entry passes through a seven-stage pipeline. Each stage validates evidence before progression. Human review remains available at every step.
AI as Perception
AI produces evidence. The system evaluates evidence. Human review remains possible.
Perceives biometric data. Outputs evidence package.
Standardized format. Confidence metrics. No authorization claim.
Evaluates evidence. Applies configurable thresholds.
Always available. Approve, reject, or request modification.
Capabilities
Identity Verification
1:N face matching, liveness detection, anti-spoofing. Evidence packages are provider-agnostic and auditable.
Hall Ticket
OCR extraction, field matching, tamper detection.
Seat Assignment
Seat-to-student mapping. Wrong-hall detection.
Attendance
Real-time attendance tracking. Entry-point logging.
Monitoring
Active sessions, gate status, capacity tracking.
Security Signals
Identity Mismatch
Biometric mismatch between live capture and enrolled identity.
Liveness Spoof
Anti-spoofing layer detected presentation attack.
Wrong Hall
Candidate attempting entry in unassigned examination hall.
Wrong Entry Point
Candidate entered through a different gate than assigned.
Repeated Failed Verification
Multiple consecutive verification failures for a session.
Document Anomaly
OCR confidence below threshold or field mismatch detected.
Architecture
ExamGuard separates what the AI sees from what the system decides. Provider output is evidence — never a direct authorization decision.
AI / Provider
Captures biometric data, outputs evidence
Evidence Package
Standardized, auditable, provider-agnostic
Decision Engine
Evaluates against configurable thresholds
Human Review
Always available with full audit trail
Principles
Minimal data collection. No biometric storage beyond enrollment hashes. Right to deletion.
Every AI decision is explainable. Evidence is logged. Thresholds are configurable.
AI assists. Humans decide. Manual override is always available with justification.
Swap face recognition providers without code changes. Evidence format is standardized.
Connect your institution's examination workflow with AI-powered verification that leaves a complete audit trail.