Biometric identification and verification, built for deployment in access control and screening environments rather than laboratory benchmarks.

The system covered the full pipeline: detection, alignment, feature extraction and matching against an enrolled gallery, with the operating point tunable between false-accept and false-reject depending on what the deployment could tolerate.

Diagram of the face recognition processing pipeline
The recognition pipeline, from capture through alignment and feature extraction to gallery matching.

Operating characteristics

The engineering question in a deployed biometric system is never a single accuracy figure. It is where you place the threshold, what each kind of error costs in that particular installation, and how the system behaves under lighting, pose and population conditions that differ from the enrolment set.

That discipline — being precise about what a system demonstrates under which conditions, rather than quoting a headline number — is the same one we now apply to validating cognitive simulation.

Chart showing recognition performance across operating thresholds
Operating characteristics across threshold settings, showing the trade-off between false accept and false reject rates.