Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Automatic Eye Detection and Its Validation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Ubiquitously supervised subspace learning
IEEE Transactions on Image Processing
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face Recognition Using Spatially Constrained Earth Mover's Distance
IEEE Transactions on Image Processing
Gabor-Based Region Covariance Matrices for Face Recognition
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
We address the challenging problem of face recognition under the scenarios where both training and test data are possibly contaminated with spatial misalignments. A supervised sparse coding framework is developed in this paper towards a practical solution to misalignment-robust face recognition. Each gallery face image is represented as a set of patches, in both original and misaligned positions and scales, and each given probe face image is then uniformly divided into a set of local patches. We propose to sparsely reconstruct each probe image patch from the patches of all gallery images, and at the same time the reconstructions for all patches of the probe image are regularized by one term towards enforcing sparsity on the subjects of those selected patches. The derived reconstruction coefficients by @?"1-norm minimization are then utilized to fuse the subject information of the patches for identifying the probe face. Such a supervised sparse coding framework provides a unique solution to face recognition with all (Here, we emphasize ''all'' because some conventional algorithms for face recognition possess partial of these characteristics.) the following four characteristics: (1) the solution is model-free, without the model learning process, (2) the solution is robust to spatial misalignments, (3) the solution is robust to image occlusions, and (4) the solution is effective even when there exist spatial misalignments for gallery images. Extensive face recognition experiments on three benchmark face datasets demonstrate the advantages of the proposed framework over holistic sparse coding and conventional subspace learning based algorithms in terms of robustness to spatial misalignments and image occlusions.