An approach towards head pose estimation is introduced based on Gabor eigenspace modeling. A Gabor filter is used to enhance pose information and eliminate other distractive information like variable face appearance or changing environmental illumination. We discuss the selection of optimal Gabor filter's orientation to each pose, which leads to more compact pose clustering. Then we use a distribution-based pose model (DBPM) to model each pose cluster in Gabor eigenspace. Thus to each pose cluster, a 2D-distance space is established where the distance from centroid (DFC) could be used to estimate head pose. Experimental results demonstrate the algorithm's robustness and generalization. We also try our algorithm on real scene sequences to detect human face and estimate its pose. In this way, user can control an intelligent wheelchair just by his head poses.