Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Generalized Low Rank Approximations of Matrices
Machine Learning
Neural Networks - 2005 Special issue: IJCNN 2005
A Novel Eye Location Algorithm based on Radial Symmetry Transform
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
MutualBoost learning for selecting Gabor features for face recognition
Pattern Recognition Letters
Is two-dimensional PCA equivalent to a special case of modular PCA?
Pattern Recognition Letters
Evaluation of transfer evidence for three-level multivariate data with the use of graphical models
Computational Statistics & Data Analysis
The BANCA database and evaluation protocol
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Isotone additive latent variable models
Statistics and Computing
Separable linear discriminant analysis
Computational Statistics & Data Analysis
Journal of Computational and Applied Mathematics
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A dimension reduction technique is proposed for matrix data, with applications to face recognition from images. In particular, we propose a factored covariance model for the data under study, estimate the parameters using maximum likelihood, and then carry out eigendecompositions of the estimated covariance matrix. We call the resulting method factored principal components analysis. We also develop a method for classification using a likelihood ratio criterion, which has previously been used for evaluating the strength of forensic evidence. The methodology is illustrated with applications in face recognition.