Several studies have shown that presentation attacks (PAs) often introduce subtle but detectable degradations in image quality, such as unnatural textures, lighting inconsistencies, or printing artifacts. These cues make Image Quality Assessment (IQA) a promising direction for improving Presentation Attack Detection (PAD) systems. This work explores the effect of including IQA in face PAD, starting with investigating the correlation between natural scene statistics (NSS)-based IQA features and PAD performance, and evaluating models that integrate explicit IQA information. While still in its early stages, this study goal is to better understand the role of image quality in face PAD and guide future improvements.