This study evaluates the feasibility, repeatability, and temporal consistency of a low-cost long-wave infrared (LWIR) thermal imaging workflow for in-vehicle driver monitoring under realistic operating conditions. Two participants were monitored during three independent 60 min driving sessions each. Facial thermal observations were obtained using a consumer-grade mobile LWIR camera operated through a smartphone application environment. Forehead-region temperature data were extracted from a manually positioned region of interest (ROI), including center-point, mean, maximum, and minimum temperature values. Geometric validation was first performed under stationary vehicle conditions in order to confirm forehead-ROI visibility and stability across multiple head orientations and posture variations. Subsequent dynamic sessions were used to evaluate cross-session repeatability and temporal behavior of sampled ROI-based thermal metrics. The results show that the facial thermal patterns remained structurally consistent across repeated sessions, while the sampled temperature trajectories exhibited generally smooth behavior without evidence of progressive within-session instability over the 60 min recordings. Although minor inter-session offsets were observed, normalized analysis confirmed preservation of the relative temporal dynamics. The findings indicate that the examined low-cost LWIR workflow can provide sufficiently stable and repeatable facial thermal observations for feasibility-level driver monitoring analysis under realistic in-vehicle conditions. The contribution of this work lies in a structured validation methodology combining geometric validation, cross-session repeatability, and temporal consistency assessment as a methodological foundation for future thermal-based driver monitoring applications.
Yordan Stoyanov (Wed,) studied this question.