Real-World Data for a Synthetic-Trained PAD Model

The Problem

For over two years, a global biometric vendor trained its PAD system entirely on synthetic datasets. They were easy to generate and scale — and they never reached certification.

Generalization: the model struggled with real-world lighting, texture, and motion artifacts.
Pre-check failures: advanced attacks, especially latex and high-quality 3D masks, consistently bypassed detection.
Stalled R&D: two years of iteration brought the system no closer to iBeta Level 2, delaying market entry.

Solution

The vendor replaced its synthetic pipeline with an iBeta-ready Level 2 dataset built to mirror certification conditions:

32,000+ high-resolution videos covering advanced attack types, including latex and composite masks.
Target-device recordings, so the data matched the exact hardware used in the lab.
Diverse actors across multiple ethnic backgrounds, filmed in real environments.
Iterative integration into the client's pipeline, with feedback rounds to close gaps found during training.

Phase	Input	Scope of Work	Quality Control
Model & Requirements Intake	Client's PAD model, target Level 2 attack scenarios	Access setup, mapping the synthetic-to-real gap across latex, composite, and 3D-mask attacks	Test scope matches the client's model and Level 2 attack matrix
Dataset Delivery	iBeta-ready Level 2 set (32,000+ videos)	Delivering advanced-attack footage recorded on the target certification device	Attack types and device coverage confirmed against iBeta conditions
Integration & Retraining	Delivered dataset + client pipeline	Folding real-world data into training, replacing synthetic-only samples	Real-world lighting, texture, and motion represented in the training set
Error Analysis	Model inference results	Accuracy breakdown by attack vector; isolating residual latex / 3D-mask misses	Systematic weak spots identified, not an aggregate score
Feedback Rounds	Error findings	Additional recordings targeting the vectors the model still missed	New data maps directly to a documented gap
Pre-Check & Handoff	Retrained model, final dataset	Level 2 pre-check; packaging data and findings for the client	Client sign-off; model cleared for lab submission

Week 1

Model & Requirements Intake

Weeks 2–4

Dataset Integration & Retraining

Weeks 4 –6

Error Analysis & Feedback Rounds

Week 7

iBeta Pre-Check & Handoff

The Results

Passed iBeta Level 2 on the first attempt.
Reliable detection across the most complex spoofing scenarios, including latex and composite masks.
2.6% accuracy gain on critical attack vectors, beyond what synthetic data reached.
Two years of stagnation resolved in two months of dataset use and retraining.

The team spent two years tuning the model, but the real constraint was the data. We added real latex and composite masks to the pipeline and accuracy improved within days. A lot of teams get caught out by device specificity, iBeta runs its tests on one exact device, so a model trained on generic footage tends to underperform when it gets there.