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
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
The Amsterdam Library of Object Images
International Journal of Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Boosted discriminant projections for nearest neighbor classification
Pattern Recognition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A framework for 3d object recognition using the kernel constrained mutual subspace method
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Randomised manifold forests for principal angle-based face recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
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Object recognition using image-set or video sequence as input tends to be more robust since image-set or video sequence provides much more information than single snap-shot about the variability in the appearance of the target subject. Constrained Mutual Subspace Method (CMSM) is one of the state-of-the-art algorithms for imageset based object recognition by first projecting the image-set patterns onto the so-called generalized difference subspace then classifying based on the principal angle based mutual subspace distance. By treating the subspace bases for each image-set patterns as basic elements in the grassmann manifold, this paper presents a framework for robust image-set based recognition by CMSM-based ensemble learning in a boosting way. The proposed Boosting Constrained Mutual Subspace Method(BCMSM) improves the original CMSM in the following ways: a) The proposed BCMSM algorithm is insensitive to the dimension of the generalized differnce subspace while the performance of the original CMSM algorithm is quite dependent on the dimension and the selecting of optimum choice is quite empirical and case-dependent; b) By taking advantage of both boosting and CMSM techniques, the generalization ability is improved and much higher classification performance can be achieved. Extensive experiments on real-life data sets (two face recognition tasks and one 3D object category classification task) show that the proposed method outperforms the previous state-of-the-art algorithms greatly in terms of classification accuracy.