A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Face Recognition
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Face recognition using a color PCA framework
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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The paper presents a problem of recognition of color facial images in the aspect of dimensionality reduction performed by means of Principal Component Analysis employing different variants of input data organization. Here, input images are represented by tensors of 3rd order and the PCA is applied for matrices derived from such tensors. Its main advantage is associated with efficient representation of images leading to the accurate recognition. The paper describes practical aspects of the algorithm and its implementation for three variants of tensor unfolding. Furthermore the impact of the number of training/testing images, the reduction ratio and the color model on the recognition accuracy is investigated. The experiments performed on Essex facial image databases showed that face recognition using this type of feature space dimensionality reduction is particularly convenient and efficient, giving high recognition performance.