Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Exemplar-Based Face Recognition from Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Dealing with occlusions in the eigenspace approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Eigen Light-Fields and Face Recognition Across Pose
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition from Video: A CONDENSATION Approach
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Identity Management in Face Recognition Systems
Biometrics and Identity Management
| Hi-index | 0.00 |
In this work we present an appearance-based 3-D Face Recognition approach that is able to recognize faces in video sequences, independent from face pose. For this we combine eigen light-fields with probabilistic propagation over time for evidence integration. Eigen light-fields allow us to build an appearance based 3-D model of an object; probabilistic methods for evidence integration are attractive in this context as they allow a systematic handling of uncertainty and an elegant way for fusing temporal information. Experiments demonstrate the effectiveness of our approach. We tested this approach successfully on more than 20 testing sequences, with 74 different individuals.