Head pose estimation by nonlinear manifold learning
Authors:
- 1 January 2004
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4 (10514651) , 462-466 Vol.4
Abstract
In This work we propose an isomap-based nonlinear alternative to the linear subspace method for manifold representation of view-varying faces. Being interested in user-independent head pose estimation, we extend the isomap model (J.B. Tenenbaum et al., 2000) to be able to map (high-dimensional) input data points which are not in the training data set into the dimensionality-reduced space found by the model. From this representation, a pose parameter map relating the input face samples to view angles is learnt. The proposed method is evaluated on a large database of multi-view face images in comparison to two other recently proposed subspace methods.Keywords
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