Personal identification systems that use face recognition work well for test images with frontal view face, but often fail when the input face is a pose view. Most face databases come from picture ID sources such as passports or driver’s licenses. In such databases, only the frontal view is available. This paper proposes a method of 2D pose-invariant face recognition that assumes the search database contains only frontal view faces. Given a non-frontal view of a test face, the pose-view angle is first calculated by matching the test image with a database of canonical faces with head rotations to find the best matched image. This database of canonical faces is used only to find the head rotation. The database does not contain images of the test face itself, but has a selection of template faces, each face having rotation images of − 45°, − 30°, − 15°, 0°, 15°, 30°, and 45°. The landmark features in the best matched rotated canonical face such as say rotation 15° and it’s corresponding frontal face of rotation 0° are used to create a warp transformation to convert the 15° rotated test face to a frontal face. This warp will introduce some distortion artifacts since some features of the non-frontal input face are not visible due to self-occlusion. The warped image is, therefore, enhanced by mixing intensities using the left/right facial symmetry assumption. The enhanced synthesized frontal face image is then used to find the best match target in the frontal face database. We test our approach using CMU Multi-PIE database images. Our method performs with acceptable and similar accuracy to conventional methods, while using only frontal faces in the test database.