Measuring cognitive load is essential for understanding driver performance. Under- and overload can result in dangerous situations on the road. Cognitive load can be estimated by monitoring the diameter and movements of the pupils, but during measurements external influences such as changes in light intensity affect pupil diameters. In this paper, we present a novel method for quantifying light intensity with a head-mounted eye-tracker by weighting pixel values around the gaze direction. We demonstrate its effectiveness in cognitive load classification systems that use pupil metrics only. 54 participants in two separate studies have carried out n-back tasks during a simple driving task in a driving simulator. The data is classified by cognitive task (baseline, 1-back, 2-back) with the Random Forest algorithm. The resulting systems are 92.5% accurate with and 85.9% accurate without gaze features available, but are unable to generalise to participants unseen in the training phase of the algorithm.