Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comparative Evaluation of Face Sequence Matching for Content-Based Video Access
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Face Recognition from Face Motion Manifolds using Robust Kernel Resistor-Average Distance
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Recognition with Image Sets Using Hierarchically Extracted Exemplars from Appearance Manifolds
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Face recognition from a single image per person: A survey
Pattern Recognition
Boosted manifold principal angles for image set-based recognition
Pattern Recognition
Grassmann discriminant analysis: a unifying view on subspace-based learning
Proceedings of the 25th international conference on Machine learning
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Manifold learning for video-to-video face recognition
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Kernel discriminant transformation for image set-based face recognition
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
Maximal Linear Embedding for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Unsupervised Image Matching Based on Manifold Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Covariance discriminative learning: A natural and efficient approach to image set classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Face Recognition Using Sparse Approximated Nearest Points between Image Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Position regularized Support Vector Domain Description
Pattern Recognition
SVStream: A Support Vector-Based Algorithm for Clustering Data Streams
IEEE Transactions on Knowledge and Data Engineering
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Image set matching attracted increasing attention in the field of pattern recognition. Recently, there are a number of effective image set-based matching methods under controlled environment. However in the more complex environment, like multi-view and illumination changed, it is still a challenging problem to develop unsupervised image set matching method to handle multi-local model data. To solve this problem, in this paper, we present a novel multi-local model image set matching method based on data description techniques. First, every image set is divided into multi-local models, and each local model corresponds to a data domain, that is, we innovatively train a support vector data domain to describe each local model by means of the excellent data description ability of support vector data domain, hence each image set can be expressed by a plurality of support vector data domain. Second, a new similarity measure based on domain-domain distance is proposed, and then the distance between two image sets is converted to integrate the distance between pair-wise domains. Finally, the proposed method is evaluated on both set-based face recognition and object classification tasks. Extensive experimental results show that the proposed method outperforms other state of the art set-based matching methods in three public video databases.