Representation and recognition in vision
Representation and recognition in vision
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Output Regularized Metric Learning with Side Information
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Semi-Supervised Learning
Face recognition with patterns of oriented edge magnitudes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Face verification using indirect neighbourhood components analysis
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Cosine similarity metric learning for face verification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Identifying Join Candidates in the Cairo Genizah
International Journal of Computer Vision
Weakly supervised learning of foreground-background segmentation using masked RBMs
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
One shot similarity metric learning for action recognition
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Learning local binary patterns for gender classification on real-world face images
Pattern Recognition Letters
Face recognition using the POEM descriptor
Pattern Recognition
Distance metric learning with eigenvalue optimization
The Journal of Machine Learning Research
High-throughput-derived biologically-inspired features for unconstrained face recognition
Image and Vision Computing
Positive semidefinite metric learning using boosting-like algorithms
The Journal of Machine Learning Research
PDSS: patch-descriptor-similarity space for effective face verification
Proceedings of the 20th ACM international conference on Multimedia
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Local higher-order statistics (LHS) for texture categorization and facial analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
A robust and efficient doubly regularized metric learning approach
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Discriminative dictionary learning with pairwise constraints
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Sparsity sharing embedding for face verification
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Semantic pixel sets based local binary patterns for face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Digital paparazzi: spotting celebrities in professional photo libraries
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Learning discriminant face descriptor for face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Object templates for visual place categorization
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Pairwise support vector machines and their application to large scale problems
The Journal of Machine Learning Research
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Evaluating the similarity of images and their descriptors by employing discriminative learners has proven itself to be an effective face recognition paradigm. In this paper we show how “background samples”, that is, examples which do not belong to any of the classes being learned, may provide a significant performance boost to such face recognition systems. In particular, we make the following contributions. First, we define and evaluate the “Two-Shot Similarity” (TSS) score as an extension to the recently proposed “One-Shot Similarity” (OSS) measure. Both these measures utilize background samples to facilitate better recognition rates. Second, we examine the ranking of images most similar to a query image and employ these as a descriptor for that image. Finally, we provide results underscoring the importance of proper face alignment in automatic face recognition systems. These contributions in concert allow us to obtain a success rate of 86.83% on the Labeled Faces in the Wild (LFW) benchmark, outperforming current state-of-the-art results.