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 Bayesian Face Recognition Based on Gaussian Mixture Models
accuracies of Bayesian face recognition based on Gaussian mixture models with several linear subspace methods based on uniform model, PCA, LDA, and Bayes. PCA is a baseline for evaluation, since it captures all kinds of major facial variation including both T and I, and does not take effort to reduce the intrapersonal variation.
Modular PCA and Probabilistic Similarity Measure for ...
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper addresses a probabilistic approach to develop a robust face recognition system to partial variations such as occlusions. Based on the statistical feature extraction methods, we take the modular PCA method which nds eigenspace not for the set of whole images but for the sets of local image patches....
PCA vs. ICA: A comparison on the FERET data set
context of a simple, baseline recognition system and the FERET face recognition database. We have tested three different distance metrics – L1 norm, L2 norm, and cosine angle - for both PCA and ICA. We find, contrary to previous reports in the literature, that PCA significantly outperforms ICA when the best...
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