Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Separating Style and Content with Bilinear Models
Neural Computation
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A unified tensor framework for face recognition
Pattern Recognition
IEEE Transactions on Neural Networks
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
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Tensorface based approaches decompose an image into its constituent factors (i.e., person, lighting, viewpoint, etc.), and then utilize these factor spaces for recognition. However, tensorface is not a preferable choice, because of the complexity of its multimode. In addition, a single mode space, except the person-space, could not be used for recognition directly. From the viewpoint of practical application, we propose a bimode model for face recognition and face representation. This new model can be treated as a simplified model representation of tensorface. However, their respective algorithms for training are completely different, due to their different definitions of subspaces. Thanks to its simpler model form, the proposed model requires less iteration times in the process of training and testing. Moreover bimode model can be further applied to an image reconstruction and image synthesis via an example image. Comprehensive experiments on three face image databases (PEAL, YaleB frontal and Weizmann) validate the effectiveness of the proposed new model.