Cross-Attribute Feature-Perceptive Driver Emotion Recognition in Real Scenarios

Driver emotion recognition has garnered significant attention due to its applications in driver emotion monitoring and regulation systems. By analyzing the driver’s facial expressions, it can effectively detect their emotional state, thereby enhancing driving safety. However, variations in lighting conditions (such as direct sunlight or shadowing) and facial occlusions (such as hair or sunglasses) still pose challenges to the system’s accuracy. To address these challenges, we propose a novel cross-attribute feature-perceptive network (CaFpNet) to enhance the driver emotion recognition performance in real-world environments by effectively utilizing multi-attribute facial features from global, local, and salient subregions and thus fully exploiting the diverse potential information provided by each facial attribute, in line with the human face perception mechanism that extracts both global and regional information. Specifically, the global-attribute feature (GaF) module focuses on the more important facial features in the overall face by expanding the number of channels to preserve features and assigning weights to different channels. Moreover, the local-attribute feature (LaF) and salient-attribute feature (SaF) modules capture regional feature information from local and salient-attribute features, respectively, focusing on fine-grained regional features and reducing the interference from irrelevant regions in feature extraction. The experimental results indicate that CaFpNet exhibits superior performance compared to various state-of-the-art approaches on two driving-scenario multi-domain emotion datasets—MLI-DER and KMU-FED. Its outstanding performance in driver emotion recognition demonstrates the potential and effectiveness in real-world applications.