User's face localization and head pose estimation play an essential role in the application of face-to-face human-computer interactive systems. In this paper, we formulate pose localization in a Bayesian framework by particle sampling. Proposal distribution fusing multiple cues such as color, shape and motion is defined to realize pose localization and tracking, which effectively enhances the performance of particle sampling. Experiment results on static images and real videos show the efficiency of our algorithm.

The paper presents a novel method for mobile robot localization using visual appearance features. A multidimensional histogram is used to describe the global appearance features of an image such as colors, edge density, gradient magnitude, textures and so on. The matching of histograms determines the location of the robot. The method has been evaluated in an indoor environment, and the system correctly determines the location of 82.9% of the input scene images.

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.