Context_Driven Emotion Recognition: Integrating Multi_Cue Fusion and Attention Mechanisms for Enhanced Accuracy on the NCAER_S Dataset

In recent years, most conventional emotion recognition approaches have concentrated primarily on facial cues, often overlooking complementary sources of information such as body posture and contextual background. This limitation reduces their effectiveness in complex, real-world environments. In this work, we present a multi-branch emotion recognition framework that separately processes facial, bodily, and contextual information using three dedicated neural networks. To better capture contextual cues, we intentionally mask the face and body of the main subject within the scene, prompting the model to explore alternative visual elements that may convey emotional states. To further enhance the quality of the extracted features, we integrate both channel and spatial attention mechanisms into the network architecture. Evaluated on the challenging NCAER-S dataset, our model achieves an accuracy of 56.42%, surpassing the state-of-the-art GLAMOUR-Net. These results highlight the effectiveness of combining multi-cue representation and attention-guided feature extraction for robust emotion recognition in unconstrained settings. The findings also highlight the importance of accurate emotion recognition for human–computer interaction, where affect detection enables systems to adapt to users and deliver more effective experiences.