A Method for Evaluating User Interface Satisfaction Using Facial Recognition Technology and a PSO-BP Neural Network

User satisfaction serves as a crucial reference for iteratively optimizing software interface designs. This paper introduces a comprehensive measurement model of user satisfaction, employing Notability and Goodnotes for case studies. The proposed model incorporates facial recognition technology to gauge the intensity of users' facial expressions while interacting with various functions of the target interface. Additionally, an experimental observation method is employed to gather objective data, including task completion time, task success rates, and operational procedures, alongside users' subjective evaluations. Leveraging objective data as input and subjective ratings as output, a user satisfaction prediction model based on a PSO-BP neural network has been devised. The results demonstrate an impressive accuracy rate of 86.26%, indicating a high accuracy in subjective perception prediction. This model has proven to be effective for measuring user satisfaction and evaluating software interface usability. Moreover, this research contributes to expanding the repertoire of user interface satisfaction evaluation methods, enhancing the objectivity of measurements and surpassing the efficiency of conventional experimental evaluation techniques. The proposed model holds practical significance for software interface usability assessment and optimization design.