Abstract
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.
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Ban, SW., Lee, M. (2005). Biologically Motivated Visual Selective Attention for Face Localization. In: Paletta, L., Tsotsos, J.K., Rome, E., Humphreys, G. (eds) Attention and Performance in Computational Vision. WAPCV 2004. Lecture Notes in Computer Science, vol 3368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30572-9_15
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DOI: https://doi.org/10.1007/978-3-540-30572-9_15
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