Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Canonical Correlation Analysis with Missing Samples
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 06
Image and Vision Computing
Inter-modality face recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Face matching between near infrared and visible light images
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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Recently, more and more approaches are emerging to solve the cross-view matching problem where reference samples and query samples are from different views. In this paper, inspired by Graph Embedding, we propose a unified framework for these cross-view methods called Cross-view Graph Embedding. The proposed framework can not only reformulate most traditional cross-view methods (e.g., CCA, PLS and CDFE), but also extend the typical single-view algorithms (e.g., PCA, LDA and LPP) to cross-view editions. Furthermore, our general framework also facilitates the development of new cross-view methods. In this paper, we present a new algorithm named Cross-view Local Discriminant Analysis (CLODA) under the proposed framework. Different from previous cross-view methods only preserving inter-view discriminant information or the intra-view local structure, CLODA preserves the local structure and the discriminant information of both intra-view and inter-view. Extensive experiments are conducted to evaluate our algorithms on two cross-view face recognition problems: face recognition across poses and face recognition across resolutions. These real-world face recognition experiments demonstrate that our framework achieves impressive performance in the cross-view problems.