Facial Recognition Threshold: Bias vs. Accuracy Simulator (2D)

A crowd streams through a top-down 2D checkpoint gate; each person's face gets a similarity score drawn from real-looking impostor/genuine statistical distributions, and the current match threshold decides who is flagged as a watchlist hit. Raising the threshold cuts false positives but misses more real matches; widening the demographic error gap reproduces the documented finding that identical algorithms and thresholds can still produce unequal false-match rates across population groups. A live distribution chart plots the actual Gaussian curves and shaded false/true-positive regions behind the threshold line, an outcome bar chart tallies every person scanned so far, and the sidebar readouts track overall precision, the per-group false-positive rate and how many wrongful stops actually get actioned once a human-review step is factored in — the same accuracy/bias/oversight trade-off at the center of the EU AI Act's biometric restrictions and the city-level facial-recognition bans debated since 2019.