Head pose estimation using Fisher Manifold learning
- 23 April 2004
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Here, we propose a new learning strategy for head pose estimation. Our approach uses nonlinear interpolation to estimate the head pose using the learning result from face images of two head poses. Advantage of our method to regression method is that it only requires training images of two head poses and better generalization ability. It outperforms existed methods, such as regression and multiclass classification method, on both synthesis and real face images. Average head pose estimation error of yaw rotation is about 4/sup 0/, which proves that our method is effective in head pose estimation.Keywords
This publication has 13 references indexed in Scilit:
- Elliptical head tracking using intensity gradients and color histogramsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Discriminant analysis of principal components for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Kernel machine based learning for multi-view face detection and pose estimationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Estimating the Support of a High-Dimensional DistributionNeural Computation, 2001
- A Global Geometric Framework for Nonlinear Dimensionality ReductionScience, 2000
- Fast, reliable head tracking under varying illumination: an approach based on registration of texture-mapped 3D modelsIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Fisher discriminant analysis with kernelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- Nonlinear Component Analysis as a Kernel Eigenvalue ProblemNeural Computation, 1998
- Probabilistic visual learning for object representationIeee Transactions On Pattern Analysis and Machine Intelligence, 1997
- The Nature of Statistical Learning TheoryPublished by Springer Science and Business Media LLC ,1995