We introduce in this paper a new face coding and recognition method which employs the Enhanced FLD (Fisher Linear Discrimimant) Model (EFM)on integrated shape (vector) and texture (`shape-jree' image) information. Shape encodes the feature geometry of a face while textureprovides a normalized shape-free image by warping the originalface image to the mean shape, i.e., the average of aligned shapes. The dimensionalities of the shape and the texture spaces are$rst reduced using Principal Component Analysis (PCA).The corresponding but reduced shape and texturefeatures are then integrated through a normalization procedure toform augmented features. The dimensionality reduction procedure, constrained by EFM for enhanced generalization, maintains a proper balance between the spectral energy needs of PCA for adequate representation, and the FLD discrimination requirements, that the eigenvalues of the within-class scatter matrix should not include small trailing values after the dimension...