Abstract: In the processing of face recognition with PCA algorithm,the image may be eligible for some kind of probability density distribution and different levels of noise pollution,so the simple distance classification is no longer effective．Maximum posteriori classification combines the parameter estimation and kernal function and Bayes theory,can take into account the probability distribution well.Under the multivariate Gaussian distribution,using it to replace the distance classification can have the better recognition rate for the images containing the different parameter values of the Gaussian noise．The standard ORL face library was used to verify this theroy,and the result shows its feasibility.

Key words: Principal component analysis,Multivariate gaussian distribution,Parameter estimation,Kernel function,Bayesian theory