One of the key remaining problems in face recognition is that of handling the variability in appearance due to changes in pose. The authors present a simple and computationally efficient 3-D pose recovery methodology. It addresses the computationally expensive problem of current generic 3-D model pose recovery methods and thus is able to be applied in real-time applications. Compared with the virtual view methods, the face identification system with the proposed pose recovery method demands much less storage space as it transforms the 2-D rotated face to the 2-D fronto-parallel view for subsequent identification rather than generating multiple virtual views for a single input face. Experiments evaluating the effectiveness of the technique are reported. The systems are compared with human performances and existing techniques.