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C. Liu and H. Wechsler, "Learning the Face Space -- Representation and Recognition". In Proc. 15 Int'l Conference on Pattern Recognition, ICPR'2000, Barcelona, Spain, September 2000.

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Using Mixture Covariance Matrices to Improve Face and.. - Thomaz, Gillies, Feitosa (2001)   (Correct)

.... making the parameter estimation quite complicated this behaviour is called the curse of dimensionality [8] Furthermore, many researchers have confirmed that the PCA representation has good generalization ability especially when the distributions of each class are separated by the mean difference [1,6,7,9]. 3 Maximum Probability Classifier The basic problem in the decision theoretic methods for pattern recognition consists of finding a set of g discriminant functions d 1 (x) d 2 (x) d g (x) where g is the number of groups or classes, with the decision rule such that if the p dimensional ....

C. Liu and H. Wechsler, Learning the Face Space Representation and Recognition . In Proc. 15 th Intl Conference on Pattern Recognition, ICPR


Using Mixture Covariance Matrices to Improve Face and - Facial Expression Recognitions   (Correct)

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C. Liu and H. Wechsler, "Learning the Face Space -- Representation and Recognition". In Proc. 15 Int'l Conference on Pattern Recognition, ICPR'2000, Barcelona, Spain, September 2000.

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