From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust principal component analysis?
Journal of the ACM (JACM)
Robust sparse coding for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Low-rank matrix recovery with structural incoherence for robust face recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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In this paper, we address the problem of robust face recognition using single sample per person. Given only one training image per subject of interest, our proposed method is able to recognize query images with illumination or expression changes, or even the corrupted ones due to occlusion. In order to model the above intra-class variations, we advocate the use of external data (i.e., images of subjects not of interest) for learning an exemplar-based dictionary. This dictionary provides auxiliary yet representative information for handling intra-class variation, while the gallery set containing one training image per class preserves separation between different subjects for recognition purposes. Our experiments on two face datasets confirm the effectiveness and robustness of our approach, which is shown to outperform state-of-the-art sparse representation based methods.