A new representation method of head images for head pose estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Robust head pose estimation using supervised manifold learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Head pose estimation based on manifold embedding and distance metric learning
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation
International Journal of Computer Vision
An adaptation framework for head-pose classification in dynamic multi-view scenarios
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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Head pose estimation is an important task for many face analysis applications, such as face recognition systems and human computer interactions. In this paper we aim to address the pose estimation problem under some challenging conditions, e.g., from a single image, large pose variation, and un-even illumination conditions. The approach we developed combines non-linear dimension reduction techniques with a learned distance metric transformation. The learned distance metric provides better intra-class clustering, therefore preserving a smooth low-dimensional manifold in the presence of large variation in the input images due to illumination changes. Experiments show that our method improves the performance, achieving accuracy within 2-3 degrees for face images with varying poses and within 3-4 degrees error for face images with varying pose and illumination changes.