Robust pose invariant face recognition using coupled latent space discriminant analysis
Computer Vision and Image Understanding
Multi-view discriminant analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Pose-robust face recognition via sparse representation
Pattern Recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Multi-feature canonical correlation analysis for face photo-sketch image retrieval
Proceedings of the 21st ACM international conference on Multimedia
Regularized latent least square regression for cross pose face recognition
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
Hi-index | 0.00 |
This paper presents a novel way to perform multi-modal face recognition. We use Partial Least Squares (PLS) to linearly map images in different modalities to a common linear subspace in which they are highly correlated. PLS has been previously used effectively for feature selection in face recognition. We show both theoretically and experimentally that PLS can be used effectively across modalities. We also formulate a generic intermediate subspace comparison framework for multi-modal recognition. Surprisingly, we achieve high performance using only pixel intensities as features. We experimentally demonstrate the highest published recognition rates on the pose variations in the PIE data set, and also show that PLS can be used to compare sketches to photos, and to compare images taken at different resolutions.