Drunk on Data: How Recurrent Fusion Models Soberingly Outperform Traditional Intoxication Detection
A checkpoint camera is not a breathalyzer. That sounds obvious, until a model reports 95.82% accuracy and everyone in the room suddenly starts imagining frictionless alcohol screening at entrances, vehicles, warehouses, airports, and campuses. This is the useful tension in Detection of Intoxicated Individuals from Facial Video Sequences via a Recurrent Fusion Model.1 The paper does not claim to measure blood alcohol concentration. It does not turn facial video into courtroom-grade evidence. What it does is more specific, and arguably more operationally interesting: it shows how a video model can combine facial geometry, temporal movement, and adaptive fusion to classify likely intoxication from short facial video clips. ...