When pristine testing conditions are used, an eyewitness's high-confidence identification from a lineup can be a reliable predictor of their identification accuracy (Wixted & Wells, 2017). Further, Grabman, Dobolyi, Berelovich, and Dodson (2019) found that high-confidence identifications are more predictive of accuracy for individuals with stronger than weaker face recognition ability. We extend this research by investigating why strong face recognizers make more informative confidence judgments and fewer high-confidence errors through the framework of two different accounts: the optimality account (Deffenbacher, 1980) and the decision processes account (e.g., Kruger & Dunning, 1999). The optimality account holds that differences in the predictive value of confidence ratings made by strong versus weak face recognizers are a result of differences in the quality of their memory representations for faces, indicating that confidence-accuracy calibration would be equated between these two groups when overall accuracy is equated, whereas the decision processes account attributes differences in calibration to strong face recognizers' superior metacognitive skills, which allow them to better evaluate their performance in the domain of face recognition. Therefore, to distinguish between these accounts, we manipulated exposure and retention interval to create conditions that produced comparable levels of identification accuracy between stronger and weaker face recognizers, and then examined their confidence-accuracy calibration. The decision processes account was supported, as differences in calibration between stronger and weaker face recognizers persisted even when overall identification accuracy was equated. Stronger face recognizers are better able to regulate their use of the confidence scale points with changes in identification accuracy.