Appearance-Based Object Detection in Space-Variant Images: A Multi-model Approach

Recently, log-polar images have been successfully used in active-vision tasks such as vergence control or target tracking. However, while the role of foveal data has been exploited and is well known, that of periphery seems underestimated and not well understood. Neverthe- less, peripheral information becomes crucial in detecting non-foveated objects or events. In this paper, a multiple-model approach (MMA) for top-down, model-based attention processes is proposed. The advantages oered by this proposal for space-variant image representations are dis- cussed. A simple but representative frontal-face detection task is given as an example of application of the MMA. The combination of appearance- based features and a linear regression-based classifier proved very eec- tive. Results show the ability of the system to detect faces at very low resolutions, which has implications in fields such as visual surveillance.