Self-organizing maps
Exemplar-Based Face Recognition from Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Probabilistic Human Recognition from Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Face Recognition from Video: A CONDENSATION Approach
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
On Importance of Nose for Face Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Face Recognition Vendor Test 2002
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Journal of Cognitive Neuroscience
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Audio-video biometric recognition for non-collaborative access granting
Journal of Visual Languages and Computing
Color face recognition for degraded face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminant clustering embedding for face recognition with image sets
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
An efficient method for face retrieval from large video datasets
Proceedings of the ACM International Conference on Image and Video Retrieval
Graph-based classification of multiple observation sets
Pattern Recognition
Kernel discriminant transformation for image set-based face recognition
Pattern Recognition
Learning neighborhood discriminative manifolds for video-based face recognition
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Video-Based face recognition using bayesian inference model
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Margin preserving projection for image set based face recognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Face recognition in videos: a graph based modified kernel discriminant analysis
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Fusing cluster-centric feature similarities for face recognition in video sequences
Pattern Recognition Letters
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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In this work, we analyze the effects of face sequence length and image quality on the performance of videobased face recognition systems which use a spatio-temporal representation instead of a still image-based one. We experiment with two different databases and consider the temporal hidden Markov model as a baseline method for the spatiotemporal representation and PCA and LDA for the imagebased one. We show that the face sequence length affects the joint spatio-temporal representation more than the static-image-based methods. On the other hand, the experiments indicate that static image-based systems are more sensitive to image quality than their spatio-temporal representation-based counterpart. The second major contribution in this work is the use of an efficient method for extracting the representative frames (exemplars) from raw video. We build an appearance-based face recognition system which uses the probabilistic voting strategy to assess the efficiency of our approach.