Eye-Based Recognition of User Traits and States-A Systematic State-of-the-Art Review.

Abstract

Eye-tracking technology provides high-resolution information about a user's visual behavior and interests. Combined with advances in machine learning, it has become possible to recognize user traits and states using eye-tracking data. Despite increasing research interest, a comprehensive systematic review of eye-based recognition approaches has been lacking. This study aimed to fill this gap by systematically reviewing and synthesizing the existing literature on the machine-learning-based recognition of user traits and states using eye-tracking data following PRISMA 2020 guidelines. The inclusion criteria focused on studies that applied eye-tracking data to recognize user traits and states with machine learning or deep learning approaches. Searches were performed in the ACM Digital Library and IEEE Xplore and the found studies were assessed for the risk of bias using standard methodological criteria. The data synthesis included a conceptual framework that covered the task, context, technology and data processing, and recognition targets. A total of 90 studies were included that encompassed a variety of tasks (e.g., visual, driving, learning) and contexts (e.g., computer screen, simulator, wild). The recognition targets included cognitive and affective states (e.g., emotions, cognitive workload) and user traits (e.g., personality, working memory). A set of various machine learning techniques, such as Support Vector Machines (SVMs), Random Forests, and deep learning models were applied to recognize user states and traits. This review identified state-of-the-art approaches and gaps, which highlighted the need for building up best practices, larger-scale datasets, and diversifying tasks and contexts. Future research should focus on improving the ecological validity, multi-modal approaches for robust user modeling, and developing gaze-adaptive systems.