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-free' image) information. Shape encodes the feature geometry of a face while texture provides a normalized shape-free image by warping the original face image to the mean shape, i.e., the average of aligned shapes. The dimensionalities of the shape and the texture spaces are first reduced using Principal Component Analysis (PCA). The corresponding but reduced shape and texture features are then integrated through a normalization procedure to form 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 dimensionality reduction procedure as they appear in the denominator.