Component Fusion for Face Detection in the Presence of Heteroscedastic Noise

Face detection using components has been proved to produce superior results due to its robustness to occlusions and pose and illumination changes. A first level of processing is devoted to the detection of individual components, while a second level deals with the fusion of the component detectors. However, the fusion methods investigated up to now neglect the uncertainties that characterize the component locations. We show that this uncertainty carries important information that, when exploited, leads to increased face localization accuracy. We discuss and compare possible solutions taking into account geometrical constraints. The efficiency and usefulness of the techniques are tested with both synthetic and real world examples.