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IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Cosine similarity metric learning for face verification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
IEEE Transactions on Circuits and Systems for Video Technology
Face alignment by Explicit Shape Regression
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Face verification in an uncontrolled environment is a challenging task due to the possibility of large variations in pose, illumination, expression, occlusion, age, scale, and misalignment. To account for these intra-personal settings, this paper proposes a sparsity sharing embedding (SSE) method for face verification that takes into account a pair of input faces under different settings. The proposed SSE method measures the distance between two input faces ${\mathbf x}_A$ and ${\mathbf x}_B$ under intra-personal settings sA and sB in two steps: 1) in the association step, ${\mathbf x}_A$ and ${\mathbf x}_B$ is represented in terms of a reconstructive weight vector and identity under settings sA and sB, respectively, from the generic identity dataset; 2) in the prediction step, the associated faces are replaced by embedding vectors that conserve their identity but are embedded to preserve the inter-personal structures of the intra-personal settings. Experiments on a MultiPIE dataset show that the SSE method performs better than the AP model in terms of the verification rate.