Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Face Recognition Using Laplacianfaces
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor feature based sparse representation for face recognition with gabor occlusion dictionary
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse representation or collaborative representation: Which helps face recognition?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi-index | 0.01 |
SRC is a recent breakthrough on occluded face recognition, but raw SRC is slow and inaccurate because both occlusion elimination and face recognition are confused and finished once in @?"1-optimization. In this paper, a reconstruction based occlusion elimination and then recognition framework is put forward. The occlusion elimination procedure is consisted of two consecutive parts, occlusion detection and face reconstruction, where SRC is only used during occlusion detection. Specifically, downsampled SRC is utilized first of all to locate possible face occlusion at low computing complexity, and then the discovered unoccluded face pixels are imported into an overdetermined equation system to reconstruct an intact face. In this approach, since occlusion detection is independent with recognition, it could be carried out on downsampled images to effectively reduce computing complexity at ignorable accuracy loss. After occlusion elimination, all state of the art general recognition approaches, such as CRC_RLS, LDA, and LPP, could be directly utilized to improve classification accuracy. The verification experiments are conducted on both simulated and genuine occlusion.