A novel algorithm for color constancy
International Journal of Computer Vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organization of Innate Face Preferences: Could Genetics Be Expressed through Learning?
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Qualitative Representations for Recognition
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
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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.