Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
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
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
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
Illumination-robust face recognition using ridge regressive bilinear models
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Feature Extraction and Uncorrelated Discriminant Analysis for High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
Unsupervised Multiway Data Analysis: A Literature Survey
IEEE Transactions on Knowledge and Data Engineering
Representing image matrices: eigenimages versus eigenvectors
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
A novel incremental principal component analysis and its application for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face Recognition by Regularized Discriminant Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
MPEG video watermarking using tensor singular value decomposition
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Experiments on lattice independent component analysis for face recognition
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Incremental face recognition for large-scale social network services
Pattern Recognition
Pose-robust face recognition via sparse representation
Pattern Recognition
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Recently, bidirectional principal component analysis (BDPCA) has been proven to be an efficient tool for pattern recognition and image analysis. Encouraging experimental results have been reported and discussed in the literature. However, BDPCA has to be performed in batch mode, it means that all the training data has to be ready before we calculate the projection matrices. If there are additional samples need to be incorporated into an existing system, it has to be retrained with the whole updated training set. Moreover, the scatter matrices of BDPCA are formulated as the sum of K (samples size) image covariance matrices, this leads to the incremental learning directly on the scatters impossible, thus it presents new challenge for on-line training. In fact, there are two major reasons for building incremental algorithms. The first reason is that in some cases, when the number of training images is very large, the batch algorithm cannot process the entire training set due to large computational or space requirements of the batch approach. The second reason is when the learning algorithm is supposed to operate in a dynamical settings, that all the training data is not given in advance, and new training samples may arrive at any time, and they have to be processed in an on-line manner. Through matricizations of third-order tensor, we successfully transfer the eigenvalue decomposition problem of scatters to the singular value decomposition (SVD) of corresponding unfolded matrices, followed by complexity and memory analysis on the novel algorithm. A theoretical clue for selecting suitable dimensionality parameters without losing classification information is also presented in this paper. Experimental results on FERET and CMU PIE (pose, illumination, and expression) databases show that the IBDPCA algorithm gives a close approximation to the BDPCA method, but using less time.