Landmark detection has proven to be a very challenging task in biometrics. In this paper, we address the task of facial component-landmark detection. By component we refer to a rectangular subregion of the face, containing an anatomical component (e.g., eye). We present a fully-automated system for facial component-landmark detection based on multi-resolution isotropic analysis and adaptive bag-of-words descriptors incorporated into a cascade of boosted classifiers. Specifically, first each component-landmark detector is applied independently and then the information obtained is used to make inferences for the localization of multiple components. The advantage of our approach is that it has robustness to pose as well as illumination. Our method has a failure rate lower than that of commercial software. Additionally, we demonstrate that using our method for the initialization of a point landmark detector results in performance comparable with that of state-of-the-art methods. All of our experiments are carried out using data from a publicly available database.