eXplainable AI(XAI) for Touch-Stroke Biometrics: Insights from SHAP

This paper presents an XAI-based framework for touch-stroke behavioural biometrics. Initially, a Random Forest classifier is trained to perform user classification, and feature importances are derived from the model's internal metrics. Subsequently, SHAP explanations are applied to obtain model-agnostic feature attributions, in both portrait and landscape modes. A comparison between the two approaches is then conducted to identify consistent patterns of feature relevance, informing the decision to exclude redundant or less influential features. The findings underscore the potential of integrating XAI into behavioural biometrics to enhance transparency and user trust.