Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attentional scene segmentation: integrating depth and motion
Computer Vision and Image Understanding
Statistical color models with application to skin detection
International Journal of Computer Vision
Face Detection Based on Color and Local Symmetry Information
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Tracking and Segmenting People in Varying Lighting Conditions Using Colour
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
A highly efficient system for automatic face region detection in MPEG video
IEEE Transactions on Circuits and Systems for Video Technology
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Feature map fusion in Visual Attention (VA) models is by definition an uncertain procedure. One of the major impediments in extending the static VA architecture proposed by Itti et al. (2000) to account for motion or other information is the lack of justification on how to integrate the various channels. We propose an innovative committee machine scheme that allows for dynamically changing the committee members (maps) and weighting them according to the confidence level of their estimation. Through this machine we handle the extensions on Itti's model; we add a motion channel and a prior knowledge channel which accounts for the conscious search performed by humans when looking for faces in a scene. The experimental results, obtained when considering face detection, show that the map fusion, through the proposed committee machine, leads to significantly better statistical results when compared with the simple skin-based face detection method.