In this paper a technical realization of an attentional mechanism extracting salient regions in face images is presented. Using the knowledge of the eye movement strategies of the human visual system the attentional mechanism localizes prominent regions in natural images. Elementary visual features are derived from high-resolution digitized images and represented in a so called topographical attention map. A top-down algorithm analyzes this map implemented as a multi-scale representation by searching the most salient features in each represented scale. The scale information is propagated from coarse scales to the next higher resolutions while the extent of the fixated region is adapted. After the extraction the selected and now locally limited regions are analyzed and interpreted. The proposed attentional mechanism is part of an image processing system identifying faces with dysmorphic signs. To improve the classification of a local analysis module, it is intended to examine these characteristic areas with specialized algorithms. This knowledge based approach shows that an artificial attention control system is capable of localizing the prominent facial areas (eyes, mouth, nose, etc.) in face images only based on elementary image features. The proposed algorithm can also be used to detect other attentive regions in any images based on appropriate features.