The purpose of Face localization is to determine the coordinates of a face in a given image. It is a fundamental research area in computer vision because it serves, as a necessary first step, any face processing systems, such as automatic face recognition, face tracking or expression analysis. Most of these techniques assume, in general, that the face region has been perfectly localized. Therefore, their performances depend widely on the accuracy of the face localization process. The purpose of this paper is to mainly show that the error made during the localization process may have different impacts which depend on the final application. We first show the influence of localization errors on the specific task of face verification and then empirically demonstrate the problems of current localization performance measures when applied to this task. In order to properly evaluate the performance of a face localization algorithm, we then propose to embed the final application (here face verification) into the performance measuring process. Using two benchmark databases, BANCA and XM2VTS, we proceed by showing empirically that our proposed method to evaluate localization algorithms better matches the final verification performance.