Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant analysis in correlation similarity measure space
Proceedings of the 24th international conference on Machine learning
Tied Factor Analysis for Face Recognition across Large Pose Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition across pose: A review
Pattern Recognition
Learning discriminative canonical correlations for object recognition with image sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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
Locally Linear Regression for Pose-Invariant Face Recognition
IEEE Transactions on Image Processing
Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition
IEEE Transactions on Image Processing
Multi-view discriminant analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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Pose variation is one of the challenging factors for face recognition. In this paper, we propose a novel cross-pose face recognition method named as Regularized Latent Least Square Regression (RLLSR). The basic assumption is that the images captured under different poses of one person can be viewed as pose-specific transforms of a single ideal object. We treat the observed images as regressor, the ideal object as response, and then formulate this assumption in the least square regression framework, so as to learn the multiple pose-specific transforms. Specifically, we incorporate some prior knowledge as two regularization terms into the least square approach: 1) the smoothness regularization, as the transforms for nearby poses should not differ too much; 2) the local consistency constraint, as the distribution of the latent ideal objects should preserve the geometric structure of the observed image space. We develop an alternating algorithm to simultaneously solve for the ideal objects of the training individuals and a set of pose-specific transforms. The experimental results on the Multi-PIE dataset demonstrate the effectiveness of the proposed method and superiority over the previous methods.