Feature-Based Affine-Invariant Localization of Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
Learning Parts-Based Representations of Data
The Journal of Machine Learning Research
Learning to generate novel views of objects for class recognition
Computer Vision and Image Understanding
The emergence of visual categories: a computational perspective
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We present a method to learn models of human heads for the purpose of detection from different viewing angles. We focus on a model where objects are represented as constellations of rigid features (parts). Variability is represented by a joint probability density function (pdf) on the shape of the constellation. In a first stage, the method automatically identifies distinctive features in the training set using an interest operator followed by vector quantization. The set of model parameters, including the shape pdf, is then learned using expectation maximization. Experiments show good generalization performance to novel viewpoints and unseen faces. Performance is above 90% correct with less than 1s computation time per image.