The problem of pose in 2 0 face recognition is widely acknowledged. Commercial systems are limited to near frontal face images and cannot deal with pose deviations larger than 15 degreesfram the frontal view. This is a problem when using face recognition for surveillance applications in which people can move ,freely. We suggest a preprocessing step to warp ,faces ,from a non ji-ontal pose to a nearfiontal pose. We use view-based active appearance models to j t to a novel face image under a random pose. The model parameters are adjusted to correct for the pose and used to reconstruct the face under a novel pose. This preprocessing makes face recognition more robust with respect to variations in the pose. An improvement in the identification rate of 60% (from 15 % to 75%) is obtained for faces under a pose of 45 degrees.