Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Motion Feature Detection Using Steerable Flow Fields
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Boosting Local Feature Based Classifiers for Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Attention links sensing to recognition
Image and Vision Computing
Journal of Cognitive Neuroscience
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
TarzaNN: a general purpose neural network simulator for visual attention modeling
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Modeling the Dynamics of Feature Binding During Object-Selective Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
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We present an attention-based face detection and localization system. The system is biologically motivated, combining face detection based on second-order circular patterns with the localization capabilities of the Selective Tuning (ST) model of visual attention [1]. One of the characteristics of this system is that the face detectors are relatively insensitive to the scale and location of the face, and thus additional processing needs to be performed to localize the face for recognition. We extend ST's ability to recover spatial information to this object recognition system, and show how this can be used to precisely localize faces in images. The system presented in this paper exhibits temporal characteristics that are qualitatively similar to those of the primate visual system in that detection and categorization is performed early in the processing cycle, while detailed information needed for recognition is only available after additional processing, consistent with experimental data and with certain theories of visual object recognition [2].