In this paper, we suggest a viewpoint-invariant face recognition model based on view-based representation. The suggested model has four stages: view-based representation, viewpoint classification, frontal face estimation and face recognition. For view-based representation, we obtained the feature space by using independent subspace analysis, the bases of which are grouped like the neurons in the brain's visual area. The viewpoint of a facial image can be easily classified by a single-layer perceptron due to view-dependent activation characteristic of the feature space. To estimate the independent subspace analysis representation of frontal face, a radial basis neural network learns to generalize the relation of the bases between two viewpoints. Face recognition relies on a normalized correlation for selecting the most similar frontal faces in a gallery. Through our face recognition experiment on XM2VTS , we obtained a face recognition rate of 89.33%.