Human–Computer Interaction System and Design Based on Expression Recognition

Human–computer interaction (HCI) has advanced into another age arising from developments in artificial intelligence that allow machines to comprehend and interpret human emotions dynamically. Modern systems confront challenges such as noisy input data and emotion classification accuracy, impeding adaptability in interactive environments. This work proposes a design of an intelligent HCI system for interpreting human emotions to improve better decision-making in intervention applications for human–computer collaboration in virtual reality (VR) applications. Initially, data acquisition of facial emotion expressions was done, which is followed by preprocessing techniques, including Savitzky–Golay filtering for noise removal and quadratic contrast stretching for contrast enhancement to improve clarity of the image. Subsequently, feature extraction via GhostNet along with the emotion classification through ShuffleNetV2, the classified emotions are integrated into the HCI system for dynamic adjustments based on user emotion, thereby fostering immersion and engagement in VR. Experimental evaluation shows the efficiently of proposed model performance with an accuracy of 99.80%, a precision of 99.77%, recall of 99.79%, and F1-score of 99.78%. These signify that the system can suggestively impact collaborative work between humans and computers, giving users personalized experiences and intelligent automation capabilities.