Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Nonparametric discriminant analysis and nearest neighbor classification
Pattern Recognition Letters
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
The Amsterdam Library of Object Images
International Journal of Computer Vision
Face Recognition with Image Sets Using Manifold Density Divergence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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This paper discusses general object recognition by using image set in the scenario where multiple shots are available for each object. As a way of matching sets of images, canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the classical parametric distribution-based and non-parametric sample-based methods. However, it is essentially an representative but not a discriminative way for all the previous methods in using canonical correlations for comparing sets of images. Our purpose is to define a transformation such that, in the transformed space, the sum of canonical correlations (the cosine value of the principle angles between any two subspaces) of the intra-class image sets can be minimized and meantime the sum of canonical correlations of the inter-class image sets can be maximized. This is done by learning a margin-maximized linear discriminant function of the canonical correlations. Finally, this transformation is derived by a novel iterative optimization process. In this way, a discriminative way of using canonical correlations is presented. The proposed method significantly outperforms the state-of-the-art methods for two different object recognition problems on two large databases: a celebrity face database which is constructed using Image Google and the ALOI database of generic objects where hundreds of sets of images are taken at different views.