Drunkenness Face Detection using Graph Neural Networks

The fatalities associated with driving while intoxicated (DWI) are on the rise, leading to a staggering twelve thousand people dying from it and nine lakh people getting arrested every year. DWIs are usually confirmed with the use of breathalyzers, which require the subject to blow into the machine. In light of the current pandemic caused by COVID-19, a susceptible individual may deny blowing into the machine. Thus, the need for a contactless method to detect if someone is drunk arises, so that suspects are prevented from taking advantage of the situation. This also assists law enforcement in the detection of DWI cases. The proposed study is the method to detect intoxication in a given suspect through Graph Neural Networks using facial landmarks. We also present a labeled dataset as a complementary dataset for intoxication detection. This dataset is the first graph-based data available for the detection of alcohol intoxication. Extensive experiments were carried out to validate this approach.