Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Performance Analysis of Linear Discriminant Classifiers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comparative Evaluation of Face Sequence Matching for Content-Based Video Access
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Exemplar-Based Face Recognition from Video
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Unified Subspace Analysis for Face Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Texture information in run-length matrices
IEEE Transactions on Image Processing
Audio-guided video-based face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Boosting multi-gabor subspaces for face recognition
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Recent advances in subspace analysis for face recognition
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
Multi-eigenspace learning for video-based face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In this paper, we develop a new video-to-video face recognition algorithm. The major advantage of the video based method is that more information is available in a video sequence than in a single image. In order to take advantage of the large amount of information in the video sequence and at the same time overcome the processing speed and data size problems we develop several new techniques including temporal and spatial frame synchronization and multi-level subspace analysis for video cube processing. The method preserves all the spatial-temporal information contained in a video sequence. Near perfect classification results are obtained on the XM2VTS face video database.