Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Face Recognition by Elastic Bunch Graph Matching
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Support Vector Regression and Classification Based Multi-View Face Detection and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Tracking Focus of Attention in Meetings
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Head Pose Estimation Using View Based Eigenspaces
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gabor Representations of Spatiotemporal Visual Images
Gabor Representations of Spatiotemporal Visual Images
Head Pose Estimation using Fisher Manifold Learning
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Pose Estimation using 3D View-Based Eigenspaces
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A kernel view of the dimensionality reduction of manifolds
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Fisher+Kernel Criterion for Discriminant Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Partial & Holistic Face Recognition on FRGC-II data using Support Vector Machine
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Face recognition using a kernel fractional-step discriminant analysis algorithm
Pattern Recognition
Gabor feature based face recognition using kernel methods
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Head pose estimation using stereo vision for human-robot interaction
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Cluster-based distributed face tracking in camera networks
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
3D head pose estimation and tracking using particle filtering and ICP algorithm
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Robust head pose estimation using supervised manifold learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Model free head pose estimation using stereovision
Pattern Recognition
Modeling and prediction of driver behavior by foot gesture analysis
Computer Vision and Image Understanding
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
Image and Vision Computing
Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
3D aided face recognition across pose variations
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Hi-index | 0.01 |
Head pose is an important indicator of a person's focus of attention. Also, head pose estimation can be used as the front-end analysis for multi-view face analysis. For example, face recognition and identification algorithms are usually view dependent. Pose classification can help such face recognition systems to select the best view model. Subspace analysis has been widely used for head pose estimation. However, such techniques are usually sensitive to data alignment and background noise. In this paper a two-stage approach is proposed to address this issue by combining the subspace analysis together with the topography method. The first stage is based on the subspace analysis of Gabor wavelets responses. Different subspace techniques were compared for better exploring the underlying data structure. Nearest prototype matching with Euclidean distance was used to get the pose estimate. The single pose estimate was relaxed to a subset of poses around it to incorporate certain tolerance to data alignment and background noise. In the second stage, the pose estimate is refined by analyzing finer geometrical structure details captured by bunch graphs. This coarse-to-fine framework was evaluated with a large data set. We examined 86 poses, with the pan angle spanning from -90^@? to 90^@? and the tilt angle spanning from -60^@? to 45^@?. The experimental results indicate that the integrated approach has a remarkably better performance than using subspace analysis alone.