Control and recognition on a humanoid head with cameras having different field of view

In this paper we study object recognition on a humanoid robotic head. The head is equipped with a stereo vision system with two cameras in each eye, where the cameras have lenses with different view angles. Such a system models the foveated structure of a human eye. To facilitate the pursuit of moving objects, we provide mathematical analysis that enables the robot to guide the narrow-view cameras toward the object of interest based on information extracted from the wider views. Images acquired by narrow-view cameras, which produce object images at higher resolutions, are used for recognition. The proposed recognition approach is view-based and is built around a classifier using nonlinear multiclass support vector machines with a special kernel function. We show experimentally that the increased resolution leads to higher recognition rates.