This paper proposes the methodology of classification architectures for FAS diagnosis tasks and shows their feasibility through experimental studies. We describe the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. The method consists of two steps: projection of face image from the original vector space to a face subspace via PCA, and then use of LDA to obtain a linear classifier. This hybrid classifier using PCA and LDA provides an effective framework for classification of FAS images as FAS-positive and FAS-negative.