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Subspace Analysis Using Random Mixture Models
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
In [1] , three popular subspace face recognition methods, PCA, Bayes, and LDA were analyzed under the same framework and an unified subspace analysis was proposed. However, since they are all based on a single Gaussian model, a global linear subspace often fails to deliver good performance on the data set with complex intrapersonal variation. They also have to face the problem caused by high dimensional face feature vector and the difficulty in finding optimal parameters for subspace analysis.
doi:10.1109/cvpr.2005.336
dblp:conf/cvpr/WangT05
fatcat:oxol2dfwk5ghdav4jcm3apdije