Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Modal Tensor Face for Simultaneous Super-Resolution and Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
Discriminant analysis and similarity measure
Pattern Recognition
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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In this work, we elaborate on a rather intuitive hypothesis: face recognition of low-resolution faces can be improved if the processes of reconstruction and recognition are considered simultaneously, instead of sequentially, without feedback or any interaction. Given a high-resolution training set, matching low-resolution probe images with good accuracy is an open problem. We have recently introduced [Hennings-Yeomans, Baker, and Kumar, CVPR, June 2008] a new framework for low-resolution face recognition that uses models from an image formation process, super-resolution priors and face feature extraction methods. By measuring how well an intermediate super-resolution reconstruction of the probe image fits into the models used in the process, the proposed matching algorithm extracts new features for recognition. In this paper, we present results for an improved design of these new features. We show that the proposed algorithm improves performance in both, identification and verification tasks on a large database of 337 subjects that also captures illumination variations.