Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Recognition with Image Sets Using Manifold Density Divergence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Person Reidentification Using Spatiotemporal Appearance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Cutting-plane training of structural SVMs
Machine Learning
Learning Discriminative Appearance-Based Models Using Partial Least Squares
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Multiple-Shot Person Re-identification by HPE Signature
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image-Set based face recognition using boosted global and local principal angles
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Optimizing Mean Reciprocal Rank for person re-identification
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
Multiple-shot human re-identification by Mean Riemannian Covariance Grid
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
Sparse approximated nearest points for image set classification
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Person re-identification by probabilistic relative distance comparison
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
On-the-fly feature importance mining for person re-identification
Pattern Recognition
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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Recently both face recognition and body-based person re-identification have been extended from single-image based scenarios to video-based or even more generally image-set based problems. Set-based recognition brings new research and application opportunities while at the same time raises great modeling and optimization challenges. How to make the best use of the available multiple samples for each individual while at the same time not be disturbed by the great within-set variations is considered by us to be the major issue. Due to the difficulty of designing a global optimal learning model, most existing solutions are still based on unsupervised matching, which can be further categorized into three groups: a) set-based signature generation, b) direct set-to-set matching, and c) between-set distance finding. The first two count on good feature representation while the third explores data set structure and set-based distance measurement. The main shortage of them is the lack of learning-based discrimination ability. In this paper, we propose a set-based discriminative ranking model (SBDR), which iterates between set-to-set distance finding and discriminative feature space projection to achieve simultaneous optimization of these two. Extensive experiments on widely-used face recognition and person re-identification datasets not only demonstrate the superiority of our approach, but also shed some light on its properties and application domain.