Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Selecting Models from Videos for Appearance-Based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
A System Identification Approach for Video-based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
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
On the results of the first mobile biometry (MOBIO) face and speaker verification evaluation
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Weighted principal component analysis
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Multi-local model image set matching based on domain description
Pattern Recognition
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We look in this work at the problem of video-based face recognition in which both training and test sets are video sequences, and propose a novel approach based on manifold learning. The idea consists of first learning the intrinsic personal characteristics of each subject from the training video sequences by discovering the hidden low-dimensional nonlinear manifold of each individual. Then, a target face video sequence is projected and compared to the manifold of each subject. The closest manifold, in terms of a recently introduced manifold distance measure, determines the identity of the person in the sequence. Experiments on a large set of talking faces under different image resolutions show very promising results (recognition rate of 99.8%), outperforming many traditional approaches.