SIAM Review
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, a new biased metric learning (BML) method is proposed for human head pose estimation problem. Traditional approaches focus on modeling a smooth low-dimensional manifold embedded in the high dimensional feature space. Such manifold-embedding methods, linear or nonlinear, suffer from one common drawback, that all neighbors are identified based on the Euclidean distance in the original feature space. However, the nature local structure of data is always corrupted by various factors in this original feature space. The proposed BML method aims at obtaining a global optimal linear transformation from the input feature space into a new semantic space which is characterized by pose angles. The metric is trained with the goal that local semantic structure of data with same label is preserved while the biased distance of differently labeled data is maximized. The learning process also reduces to a convex optimization by formulating it as a semidefinite problem (SDP). Numerous experiments demonstrate the superiority of our BML method over several current states of art approaches on publicly available dataset.