Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Nonlinear Approach for Face Sketch Synthesis and Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discriminant analysis in correlation similarity measure space
Proceedings of the 24th international conference on Machine learning
A Novel Method of Combined Feature Extraction for Recognition
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Face Photo-Sketch Synthesis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructing nonlinear discriminants from multiple data views
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Matching Forensic Sketches to Mug Shot Photos
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inter-modality face recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Learning discriminative canonical correlations for object recognition with image sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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
Coupled information-theoretic encoding for face photo-sketch recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Circuits and Systems for Video Technology
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Generalized Multiview Analysis: A discriminative latent space
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Regularized latent least square regression for cross pose face recognition
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The same object can be observed at different viewpoints or even by different sensors, thus generating multiple distinct even heterogeneous samples. Nowadays, more and more applications need to recognize object from distinct views. Some seminal works have been proposed for object recognition across two views and applied to multiple views in some inefficient pairwise manner. In this paper, we propose a Multi-view Discriminant Analysis (MvDA) method, which seeks for a discriminant common space by jointly learning multiple view-specific linear transforms for robust object recognition from multiple views, in a non-pairwise manner. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations of the low-dimensional embeddings from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, an analytical solution can be achieved by using the generalized eigenvalue decomposition. The proposed method is applied to three multi-view face recognition problems: face recognition across poses, photo-sketch face recognition, and Visual (VIS) image vs. Near Infrared (NIR) image face recognition. Evaluations are conducted respectively on Multi-PIE, CUFSF and HFB databases. Intensive experiments show that MvDA can achieve a more discriminant common space, with up to 13% improvement compared with the best known results.