FE-CTGCN: Topology Channel Graph Convolution Network Based on Feature Enhancement for Drowsiness Driving Detection

To reduce traffic accidents caused by drowsy driving, computer vision techniques have been widely adopted to analyze facial cues and assess driver fatigue levels. However, existing methods suffer from several limitations. Most approaches extract features from only one or two facial regions (e.g., the eyes and mouth), which restricts their ability to capture variations in drowsiness expression across different fatigue levels within the same driver, as well as individual differences among drivers. Moreover, although facial regions are inherently structured, current models typically lack structural awareness, resulting in weakly structured feature representations that may lose subtle drowsiness-related details. To address these issues, we propose a Feature-Enhanced Channel Topology Graph Convolutional Network (FE-CTGCN). The proposed framework consists of three key modules: the Feature-Enhanced Global–Local Information Module (FEGL), the Multi-source Information Representation Module (MSIR), and the Channel-wise Topology Graph Convolution Module (CTGCN). The FEGL module extracts visual features from five local facial regions and the entire face, with an emphasis on enhancing discriminative patterns across different fatigue states. The MSIR module introduces an attention-based feature fusion mechanism that not only integrates multi-source features via attention but also captures temporal dynamics, enabling effective modeling of correlations between global and local facial cues. Together, FEGL and MSIR address intra-driver variations across drowsiness levels and inter-driver differences. In addition, the CTGCN module constructs a topology-aware graph where nodes correspond to the five local facial features and the global facial representation. By modeling spatial relationships among these nodes, it facilitates structured information exchange and builds a strongly structured facial feature space that enhances internal feature integration. Experimental results demonstrate that FE-CTGCN achieves superior detection performance compared to existing methods, validating its effectiveness for driver drowsiness detection. To facilitate reproducibility and further research, the source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zzs-code/FECTGCN.git</uri>