ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
MutualBoost learning for selecting Gabor features for face recognition
Pattern Recognition Letters
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Fusing Gabor and LBP feature sets for kernel-based face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Gabor-Eigen-Whiten-Cosine: a robust scheme for face recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Compact binary patterns (CBP) with multiple patch classifiers for fast and accurate face recognition
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
One shot similarity metric learning for action recognition
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Retrieval-based face annotation by weak label regularized local coordinate coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
An on-line learning method for face association in personal photo collection
Image and Vision Computing
Random forests for metric learning with implicit pairwise position dependence
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PDSS: patch-descriptor-similarity space for effective face verification
Proceedings of the 20th ACM international conference on Multimedia
A unified learning framework for auto face annotation by mining web facial images
Proceedings of the 21st ACM international conference on Information and knowledge management
Supervised earth mover's distance learning and its computer vision applications
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Bayesian face revisited: a joint formulation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Motion interchange patterns for action recognition in unconstrained videos
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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
Exploring the similarities of neighboring spatiotemporal points for action pair matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Face verification is the task of deciding by analyzing face images, whether a person is who he/she claims to be. This is very challenging due to image variations in lighting, pose, facial expression, and age. The task boils down to computing the distance between two face vectors. As such, appropriate distance metrics are essential for face verification accuracy. In this paper we propose a new method, named the Cosine Similarity Metric Learning (CSML) for learning a distance metric for facial verification. The use of cosine similarity in our method leads to an effective learning algorithm which can improve the generalization ability of any given metric. Our method is tested on the state-of-the-art dataset, the Labeled Faces in the Wild (LFW), and has achieved the highest accuracy in the literature.