This paper introduces a framework that employs the Fisher linear discriminant model (FLDM) and classifier (FLDC) on integrated facial appearance and facial expression features. The principal component analysis (PCA) is firstly applied for dimensionality reduction. The normalized fusion method is then applied to the reduced lower dimensional subspaces of these two features. Finally, the FLDM is used for generalizing the most expressive and discriminant feature space for enhancing better generalization performance. Experimental results show that 1) the integrated features of the facial appearance and facial expressions carry the most expressive and discriminant information and 2) the intra-personal variation, indeed, can assist the extra-personal separation. In particular, the proposed method achieves 100% for our database recognition accuracy using only 9 features.