Deep Learning-Based Facial Emotion Recognition for Advanced Human-Computer Interaction

Deep learning technology establishes advanced methods for enhancing Human-Computer Interaction systems by providing machines with the ability to sense and understand human emotional states. The capability's value stems from its applications, including healthcare situations alongside virtual assistants, gaming interfaces, and educational platforms seeking personalized functions. We present a deep learning framework that unites multimodal data fusion with Convolutional Neural Network(CNN) to enhance emotion recognition systems while improving performance and interpretability. The proposed framework undergoes testing against three benchmark emotion datasets: FER2013, AffectNet, and CK+ dataset. Our experimental findings show that the proposed system delivers a 92.5% accuracy rate for emotion recognition which exceeds traditional unimodal models by a maximum of 12% improvement. Context-aware modeling achieves a 15% performance boost during real-world applications while lightweight architectural design reduces system processing requirements by 25% to support edge device runtime operations. Through explainable AI integration users can see how the model functions to make decisions therefore establishing both trust and ethical practices in use. This research delivers substantial progress in emotion recognition study through an approach that establishes a deployable system that maintains both operational effectiveness and ethical operations across multiple Human-Computer Interaction application domains.