Biologically Motivated Visual Selective Attention for Face Localization

We propose a new biologically motivated model to localize or detect faces in natural color input scene. The proposed model integrates a bottom-up selective attention model and a top-down perception model. The bottom-up selective attention model using low level features sequentially selects a candidate area which is preferentially searched for face detection. The top-down perception model consists of a face spatial invariant feature detection model using ratio template matching method with training mechanism and a face color perception model, which is to model the roles of the inferior temporal areas and the V4 area, respectively. Finally, we construct a new face detection model by integration of the bottom-up saliency map model, the face color perception model and the face spatial invariant feature detection model. Computer experimental results show that the proposed model successfully indicates faces in natural scenes.