Although facial landmark localization (FLL) approaches are becoming increasingly accurate in identifying facial components, one question remains unanswered: what is the impact of these approaches on subsequent, related tasks? In this paper, we focus on facial expression recognition (FER), where facial landmarks are used for face registration, which is a common usage. Since the common datasets for facial landmark localization do not allow for a proper measurement of performance according to the different difficulties (e.g., pose, expression, illumination, occlusion, motion blur), we also quantify the performance of recent approaches in the presence of head pose variations and facial expressions. Finally, we conduct a study of the impact of these approaches on FER. We show that the landmark accuracy achieved so far by optimizing the euclidean distance does not necessarily guarantee a gain in performance for FER. To deal with this issue, we propose a new evaluation metric for FLL that is more relevant to FER.