Topology representing networks
Neural Networks
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Radial basis function networks
The handbook of brain theory and neural networks
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
Automatic Video-based Person Authentication Using the RBF Network
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Non-intrusive Person Authentication for Access Control by Visual Tracking and Face Recognition
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Learning Graphical Models of Images, Videos and Their Spatial Transformations
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Towards unconstrained face recognition from image sequences
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
PersonSpotter - Fast and Robust System for Human Detection, Tracking and Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Recognition from Video: A CONDENSATION Approach
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Appearance-Based 3-D Face Recognition from Video
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Computer Vision and Image Understanding - Special issue on Face recognition
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Combining appearance and motion for face and gender recognition from videos
Pattern Recognition
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Learning personal specific facial dynamics for face recognition from videos
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
From still image to video-based face recognition: an experimental analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Face recognition from still images to video sequences: a local-feature-based framework
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Probabilistic model-based background subtraction
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Probabilistic model-based background subtraction
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Face recognition using more than one still image: what is more?
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Spatio-temporal embedding for statistical face recognition from video
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Face recognition from images with high pose variations by transform vector quantization
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Multi-eigenspace learning for video-based face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Fusing cluster-centric feature similarities for face recognition in video sequences
Pattern Recognition Letters
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A new exemplar-based probabilistic approach for face recognition in video sequences is presented. The approach has two stages: First, Exemplars, which are selected representatives from the raw video, are automatically extracted from gallery videos. The exemplars are used to summarize the gallery video information. In the second part, exemplars are then used as centers for probabilistic mixture distributions for the tracking and recognition process. A particle method is used to compute the posteriori probabilities. Probabilistic methods are attractive in this context as they allow a systematic handling of uncertainty and an elegant way for fusing temporal information.Contrary to some previous video-based approaches, our approach is not limited to a certain image representation. It rather enhances known ones, such as the PCA, with temporal fusion and uncertainty handling. Experiments demonstrate the effectiveness of each of the two stages. We tested this approach on more than 100 training and testing sequences, with 25 different individuals.