Head pose estimation from digital images consists of locating a person's head and estimating the orientation of its three degrees of freedom (Yaw, Pitch and Roll). This task has been considered an important research task for decades. Over the years, many techniques have been proposed to solve this problem. They can be categorized in two main classes: Model-based approaches and Appearance-based approaches. The Model-based approaches are fast and simple, but sensitive to occlusion and usually require high resolution images which may be not available in many applications such as driver monitoring or video surveillance. Appearance-based approaches suffer from information about identity and lighting which are contained in the face appearance. We propose an approach to select a set of features from the symmetrical parts of the face. The approach does not need the location of interest points on face and is robust to partial occlusion. The size of bilateral symmetrical area of the face is a good indicator of the Yaw head pose. We train a Decision Tree model in order to recognize head pose with regard to the areas of symmetry.