Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Robust Head Pose Estimation Using LGBP
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Illumination and Person-Insensitive Head Pose Estimation Using Distance Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Head Pose estimation on low resolution images
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Evaluation of head pose estimation for studio data
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Neural network-based head pose estimation and multi-view fusion
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Robust head pose estimation using supervised manifold learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Phase space for face pose estimation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
An integrated two-stage framework for robust head pose estimation
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Head Yaw Estimation From Asymmetry of Facial Appearance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On partial least squares in head pose estimation: How to simultaneously deal with misalignment
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing'04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods.