A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized low rank approximations of matrices
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multilinear Independent Components Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Algorithm 862: MATLAB tensor classes for fast algorithm prototyping
ACM Transactions on Mathematical Software (TOMS)
IEEE Transactions on Neural Networks
Face recognition using new image representations
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Learning-based image representation and method for face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Feature Fusion Using Multiple Component Analysis
Neural Processing Letters
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In this paper we present a face recognition method based on multiway analysis of color face images, which is robust to varying illumination conditions. Illumination changes cause large variations on color in face images. The main idea is to extract features with minimal color variations but with retaining image spatial information. We construct a tensor of color image ensemble, one of its coordinate reflects color mode, and employ the higher-order SVD (a multiway extension of SVD) of the tensor to extract such features. Numerical experiments show that our method outperforms existing subspace analysis methods including principal component analysis (PCA), generalized low rank approximation (GLRAM) and concurrent subspace analysis (CSA), in the task of face recognition under varying illumination conditions. The superiority is even more substantial in the case of small training sample size.