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Viewpoint-invariant learning and detection of human heads

Weber, M. and Einhäuser, W. and Welling, M. and Perona, P. (2000) Viewpoint-invariant learning and detection of human heads. In: Fourth IEEE International Conference on Automatic Face and Gesture Recognition. IEEE Computer Society , Los Alamitos, CA, pp. 20-27. ISBN 0-7695-0580-5.

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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 the 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 1 s computation time per image.

Item Type:Book Section
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Perona, P.0000-0002-7583-5809
Additional Information:© 2000 IEEE. Date of Current Version: 06 August 2002.
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INSPEC Accession Number6577267
Record Number:CaltechAUTHORS:20111130-141116001
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Official Citation:Weber, M.; Einhauser, W.; Welling, M.; Perona, P.; , "Viewpoint-invariant learning and detection of human heads," Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on , vol., no., pp.20-27, 2000 doi: 10.1109/AFGR.2000.840607 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:28256
Deposited By: Tony Diaz
Deposited On:18 Jan 2012 23:53
Last Modified:09 Nov 2021 16:55

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