Use this checklist to ensure your liveness evaluation mirrors real-world attacks. It follows ISO/IEC 30107-3 terminology and Axon Labs’ practical taxonomy of physical PAIs

Include these attack families (with minimum variation)

  • Printed: flat, cutouts, cylindrical wraps, 3D paper heads; ≥5 materials/finishes, multiple printers; vary distance/angles/lighting
  • Screen replay: phone/tablet/laptop/monitor; ≥4 screen types Ă— 3 brightness levels; front vs external camera. Example: Replay Attacks Dataset by Axon Labs
  • Photo-on-actor: flat or wrapped; ≥5 actors; 2–3 fixation methods; with/without real accessories
  • 3D masks: resin, latex, silicone, hyper-real; ≥2 manufacturers per subclass; wigs/glasses/beards; turning sequences. Example: iBeta Level 2 Dataset
  • Textile/fabric masks: printed balaclavas; hood + glasses combos; ≥3 fabric types; indoor/outdoor; different motion levels

Image: Axon Labs physical face-attack taxonomy — printed, cutout, cylindrical, replay, photo-on-actor, resin/latex/silicone masks, textile masks

Diversity that keeps metrics honest

  • Dataset scale: ≥1,000 attack videos total; for robustness, aim for ≥1,000 per PAI category
  • People/devices/scenes: >100 participants; ≥3 cameras (2 phones + 1 laptop/external webcam); bright/dim/backlight; indoor & outdoor
  • Capture & pose: 24/30/60 fps; low/med/high bitrate; 30–80 cm; yaw/pitch/roll; with & without glasses/facial hair/hood
  • Metadata (required for every clip): PAI type/subclass, material/brand, printer/screen model & settings, distance, lighting, operator, date/series, outcome (success/fail)

Useful references: ISO/IEC 30107-3, FIDO Alliance biometric requirements, and iBeta PAD testing

Reporting that procurement can trust

  • Publish APCER, BPCER, ACER, EER per class and per device
  • Map to ISO/IEC 30107-3; explicitly show performance on unknown PAIs

iBeta PAD — how to operationalize it

  • Understand the scope: formal lab evaluation at a defined operating point and test set; not a guarantee for unknown PAIs or non-physical threats
  • Make it actionable: reproduce on target devices; extend with this checklist; report per-class APCER and overall ACER; verify threshold on held-out data; log latency/TPS on production hardware
  • Avoid common mistakes: replay vs print confusion; low variability; too few matching genuine sessions

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