Face detection using biologically motivated saliency map model

We propose a new biologically motivated model to localize or detect faces in natural color input scene. The proposed model integrates a bottom-up saliency mechanism for extracting features from an input image and a top-down perceptual mechanism for detecting faces using the results of the bottom-up processing. For bottom-up feature extraction, we consider the roles of cells in our visual receptor for edge detection and cone opponency, and also reflect the roles of the lateral geniculate nucleus to find a symmetrical property of an interesting object such as shape and pattern. Also, independent component analysis (ICA) is used to find a filter that can generate a salient region from feature maps constructed by edge, color opponency and symmetry information, which models the role of redundancy reduction in the primary visual cortex. For the top down perceptional processing to detect faces, we partially model the role of the inferior temporal areas, which plays an important role for face recognition. Computer experimental results show that the proposed model successfully indicates faces in natural scenes.