Automatic face recognition was an active research area in the last decade. With the increased importance of security and organization, identification and authentication methods were developed into a key technology in various areas such as entrance control in building. Face recognition method based on probabilistic principle component analysis (PPCA) was proposed. However, a notable feature of traditional PCA was the absence of an associated probabilistic model for the observed data. A probabilistic formulation of PCA from a Gaussian latent variable model was obtained, which was closely related to statistical factor analysis. The parameters of PPCA could be determined using EM algorithm. In experiments, the proposed methods have been successfully evaluated using two different datasets. The experimental results show that the face recognition method based on PPCA is superior to the method based on the traditional PCA.