Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Face authentication with Gabor information on deformable graphs
IEEE Transactions on Image Processing
Selecting discriminant eigenfaces for face recognition
Pattern Recognition Letters
Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications
International Journal of Computer Vision
Assessment of regional myocardial function via statistical features in MR images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Expert Systems with Applications: An International Journal
Image recognition with LPP mixtures
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Expert Systems with Applications: An International Journal
Survey: Subspace methods for face recognition
Computer Science Review
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Linear discriminant analysis (LDA) provides the projection that discriminates data well, and shows a good performance for face recognition. However, since LDA provides only one transformation matrix over the whole data, it is not sufficient to discriminate complex data consisting of many classes with high variations, such as human faces. To overcome this weakness, we propose a new face recognition method based on the LDA mixture model, where the set of all classes are partitioned into several clusters and we obtain a transformation matrix for each cluster. This accurate and detailed representation will improve classification performance. Simulation results of face recognition show that LDA mixture model outperforms PCA, LDA, and PCA mixture model in terms of classification performance.