Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Some Thoughts on the QZ Algorithm for Solving the Generalized Eigenvalue Problem
ACM Transactions on Mathematical Software (TOMS)
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Gabor-Eigen-Whiten-Cosine: a robust scheme for face recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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This paper proposes a LDA/QZ algorithm and its combination of Gabor Filter-based features for the face recognition. The LDA/QZ algorithm follows the common “PCA+LDA” framework, but it has two significant virtues compared with previous algorithms: 1) In PCA step, LDA/QZ transforms the feature space into complete PCA space, so that all discriminatory information is preserved, and 2) In LDA step, the QZ-decomposition is applied to solve the generalized eigenvalue problem, so that LDA can be performed stably even when within-class scatter matrix is singular. Moreover, the Gabor Filter-based Features and the new LDA/QZ algorithm are combined for face recognition. We also performed comparative experimental studies of several state-of-art dimension reduction algorithms and their combinations of Gabor feature for face recognition. The evaluation is based on six experiments involving various types of face images from ORL, FERET, and AR database and experimental results show the LDA/QZ algorithm is always the best or comparable to the best in term of recognition accuracy.