Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Automatic Face Recognition for Film Character Retrieval in Feature-Length Films
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Taking the bite out of automated naming of characters in TV video
Image and Vision Computing
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Face Annotation Using Transductive Kernel Fisher Discriminant
IEEE Transactions on Multimedia
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper addresses semi-automatic annotation of faces in personal videos. Different from previous offline annotation systems, this paper studies online annotation of faces. During an annotation session, few annotations are requested from the user only for some part of the video online. These annotations are used to train a system that will perform annotation automatically for the rest of the video. The automatic annotation results are presented to the user during the same session and the user is allowed to correct any automatic annotation mistakes. Thus, only fast and accurate face recognition methods are considered. Instead of batch learning, which has been used in the existing annotation systems, this paper proposes sequential learning methods to be used as face classifiers. Different classification methods are tested with various feature extraction methods using the same database so that a fair comparison is made among them. The results are evaluated in terms of recognition accuracies and execution time requirements.