  Although facial landmark localization (FLL) approaches are becoming
increasingly accurate for characterizing facial regions, one question remains
unanswered: what is the impact of these approaches on subsequent related tasks?
In this paper, the focus is put on facial expression recognition (FER), where
facial landmarks are used for face registration, which is a common usage. Since
the most used 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, a study of the impact of these
approaches on FER is conducted. We show that the landmark accuracy achieved so
far optimizing the conventional 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 adapted to FER.
