Using hypergraphs instead of traditional graphs lets you capture complex, multi-way relationships in facial expressions, while bidirectional state space models efficiently process long video sequences—enabling accurate fatigue detection on edge devices.
This paper proposes HST-HGN, a neural network for detecting driver fatigue from video that combines hypergraph networks (to model complex facial relationships) with state space models (to efficiently track facial changes over time). The approach balances accuracy with computational efficiency, making it practical for real-time deployment in vehicles.